flameox

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations (readOnlyHint=true, idempotentHint=true, destructiveHint=false) already carry the safety profile, and the description does not contradict them. The description adds the entity scope being analyzed but does not disclose behaviors such as cross-run comparison via comparison_run_or_artifact or how outputs are aggregated.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single ten-word sentence that front-loads the verb and communicates the full scope with zero filler. Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present and annotations covering the read-only safety profile, the one-sentence description is adequate for the core use case. However, the comparison_run_or_artifact parameter implies a comparison capability that is never mentioned, and there is no context on how limit or phase shape the analysis, leaving an agent to guess at meaningful invocation options.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is exactly 50%: limit and run_or_artifact are described while phase and comparison_run_or_artifact are not. The description text itself adds no parameter-level meaning, so it neither compensates for the undocumented parameters nor adds value beyond what the schema already states.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Analyze') and a specific resource scope ('runtime launches, graph launches, kernels, and idle gaps'), which makes the tool's function clear and distinguishes it from siblings like analyze_memory or analyze_scaling that target different artifact types. It stops short of a 5 because it never names alternatives or explicitly explains what differentiates it from the other analyze_* siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives no guidance on when to use this tool versus the large sibling set (e.g., analyze_nsight_compute, analyze_hotspots, extract_perfetto), and no context on when to supply phase or comparison_run_or_artifact. An agent must infer usage entirely from the name and schema.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds the 'bounded' nature and pagination behavior with next_cursor, which is useful, but it does not add substantial context beyond what annotations and the cursor parameter description already convey.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with no filler. The primary action is front-loaded, and the second sentence delivers actionable pagination and preservation guidance. Every phrase earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The output schema exists and all parameters are described in the schema, so return-value documentation is not the description's burden. The description covers the key behavioral loop (cursor paging) and points to record_analysis for persistence. It could be slightly more explicit about what collections are available, but the schema enum fills that gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters. The description reinforces cursor behavior but adds little new parameter meaning beyond the schema's 'Opaque next_cursor' explanation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Inspect') and resource ('one bounded execution-evidence collection'), making the core action clear. It does not explicitly differentiate from the many analyze_* siblings, but the bounded-collection framing distinguishes it from broader summarization or extraction tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear operational guidance: repeat the same query when next_cursor is present, and use record_analysis to preserve a result. It names an alternative tool for preservation, though it does not provide explicit when-not-to-use conditions against other evidence inspection tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so 'read-only' mostly restates structured data. The description adds useful context about ordering after list_runs discovery, but it does not clarify how a failure cohort is determined or what happens when the filter is omitted.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is one tightly front-loaded sentence covering action, target, mode, and workflow placement. Every phrase carries intent, and there is no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite the output schema, the description leaves critical invocation details unstated: how to construct the 'explicit filtered failure cohort,' whether filter may be omitted, and which statuses count as failures. For a tool with a large nested RunFilter and zero parameter documentation, this is a significant gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate, but it only hints at the filter via 'explicit filtered failure cohort' and says nothing about the required 'limit' parameter. The large RunFilter object's fields are left unexplained, forcing the agent to infer semantics from enum names like 'failed' and 'timed_out'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action ('Analyze'), a resource ('failure cohort'), and a workflow position ('after list_runs discovery'), making the tool's purpose clear. It does not explicitly contrast it with sibling analyze_* tools, but the 'failures' scoping provides enough differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

'After list_runs discovery' gives an explicit sequencing cue, and 'read-only' tells the agent this is a post-discovery analysis operation. It lacks explicit exclusions or named alternatives, but the context is clear enough for an agent to know when to invoke it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide strong safety signals: readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds no contradictory claim and contributes 'bounded' and 'source-linked' context, but it does not discuss result ordering, pagination, or other behaviors; this is acceptable because the output schema and annotations carry that weight.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence with no filler. It front-loads the action, resource, and scope, then appends a necessary routing instruction that disambiguates from sibling tools.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given a 2-parameter schema with full coverage, a rich annotation set, an output schema, and an explicit sibling-routing clause, the description is complete for its purpose. There is no missing information an agent needs to select or invoke this tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, with both parameters already documented ('run_or_artifact' with discovery via list_runs/list_artifacts, and 'limit' with range 1-1000). The description adds no parameter-specific detail beyond what the schema provides, so the baseline score of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Analyze') and a specific resource class ('sampled-profile runs or artifacts') with a clear qualifier ('bounded source-linked hotspots'). It also distinguishes itself from benchmark-sample analysis by naming extract_pyperf and query_measurements, making its scope easy to identify among many sibling analysis tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use the tool ('sampled-profile runs or artifacts for bounded source-linked hotspots') and gives an explicit alternative for a distinct case: 'use extract_pyperf/query_measurements for benchmark_samples instead.' This is direct when/when-not guidance rather than merely implied usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so no safety disclaimer is needed from the description. The description adds useful behavioral scope by naming the evidence dimensions analyzed (peak, retained-end, allocation), which complements the annotation-provided safety profile. It does not discuss sorting or pagination, but that is a minor gap given the strong annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single compact sentence that front-loads the verb and resource and then enumerates the key evidence categories. There is no filler, redundancy, or restatement of the tool name.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with a nested query object and many related analyze/extract siblings, the description is somewhat thin: it does not mention that the memory profile must already be extracted, nor when to prefer this over extract_memray/get_extraction. The rich input schema and annotations carry a lot of weight, but the missing selection context keeps this from being fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema descriptions already cover the two required parameters: limit and run_or_artifact. The tool description adds loose semantic hints connecting 'peak', 'retained-end', and 'allocation' to the MemoryAllocationView enum values, but it does not explain the query object's filtering or ranking options. At 67% schema coverage, the description partially compensates but leaves the query behavior largely to the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: 'Analyze memory-profile runs or artifacts'. It further narrows the purpose to 'peak, retained-end, and allocation evidence,' which clearly separates it from the many analyze_* siblings that target CPUs, GPUs, PyTorch, failures, or scaling.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives no guidance on when to use this tool versus related siblings like extract_memray, get_extraction, or other analyze_* tools. It does not state prerequisites, exclusions, or conditions under which a sibling should be chosen instead. Some context is implied by the word 'memory-profile,' but no explicit routing is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly/idempotent/non-destructive, so the description does not need to restate safety. It adds value by disclosing that the result is bounded and what it contains (qualification, coverage, provenance, recapture selection), plus the external behavior of not reopening the .ncu-rep. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the core action and followed by a compact result summary. No filler or redundant restatement of parameters.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With a full input schema, output schema, and annotations, the description is sufficient: it explains the tool's scope and result shape. It could name the upstream extraction sibling or define 'rule facts', but this is not necessary for choosing or invoking the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%: both parameters (limit, run_or_artifact) are described in the schema, including discovering IDs via list_runs/list_artifacts. The description adds no additional parameter-specific semantics beyond the schema, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description identifies a specific verb ('Analyze'), a specific resource ('persisted Nsight Compute rule facts'), and an operative constraint ('without reopening the native .ncu-rep'). This distinguishes it from extraction-oriented siblings such as extract_nsight_compute, even without naming them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'persisted ... without reopening the native .ncu-rep' gives a clear context: use this after facts are already persisted, rather than for direct native-file extraction or recapture. It doesn't enumerate exclusions or alternative tool names, but the context is strong enough to route an agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the description adds value by disclosing the 'never extracts implicitly' behavior and the recovery flow when data is missing. This clarifies what the tool will and will not do beyond the structured annotations, though it does not go into deep detail about output behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no wasted words. The primary action is front-loaded, and the important behavioral constraint and alternative routing occupy the second sentence, keeping the structure tight and scannable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only analysis tool with full schema coverage, a rich output schema, and clear annotations, this description is complete. It covers what the tool does, its safe read-only posture, the missing-data fallback, and the exact sibling to invoke in that case. Nothing essential is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the parameters are already fully documented in the input schema. The description adds no additional parameter-level detail beyond naming the torch.profiler source, which matches the schema. Baseline 3 is appropriate because the schema carries the explanatory burden.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool summarizes normalized Perfetto evidence from a torch.profiler run or artifact, using a specific verb and resource. It also distinguishes itself from extract_perfetto by explicitly noting it 'never extracts implicitly,' which helps an agent separate analysis from extraction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit routing guidance: if normalized rows are absent, follow the recovery result and call extract_perfetto for the exact run. This directly tells the agent when to use this tool versus a sibling, which is exactly the kind of exclusionary guidance needed.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is known. The description adds useful context by clarifying that no trial collection occurs, but it does not go further into output behavior or other side effects; this matches the expected bar when annotations cover the core behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is one tight sentence, front-loads the action, and contains no filler. Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple: one required parameter, rich annotations, and an output schema. The description, combined with annotations and output schema, gives an agent everything needed to invoke it correctly; return-value details are already covered by the output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description carries the burden for parameter semantics, but it only partially compensates by saying 'existing experiment,' implying experiment_id must reference an already-created experiment. The single self-descriptive parameter plus this context is adequate, yet the description does not specify formats, sources, or further constraints beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Summarize'), a clear resource ('an existing experiment'), and a distinguishing scope ('without collecting missing trials'). This lets an agent immediately tell it apart from trial-collection tools like run_experiment and plan_experiment without opening their schemas.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly conveys when to use this tool: when an experiment already exists and a summary is needed without gathering additional trials. It provides an implicit exclusion ('without collecting missing trials') though it does not explicitly name alternative tools for when trials are missing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare idempotentHint=true and destructiveHint=false, and the description adds 'cleanup' and 'request' nuance without contradicting them. It does not explain what cleanup entails or whether the cancellation is asynchronous or can partially fail, so the added behavioral context is modest.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single, front-loaded sentence that names the action and the object with no filler. Every phrase earns its place, including the 'server-owned' scoping.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema, the return format is already handled, and the annotations cover idempotency and destructive profile. However, the description omits guidance on when cancellation is appropriate and what cleanup actually affects, leaving the agent to fill gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and the description never explains operation_id, its format, or where to obtain it. The parameter is only self-evident from its name and the tool's purpose, so the description does not compensate for the missing schema documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('cancel') with a specific resource ('capability setup operation') and adds the 'server-owned' scope. This clearly distinguishes it from siblings like start_capability_setup and get_capability_setup.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The intended use is implied: use this tool when a capability setup operation should be cancelled and cleaned up. However, it provides no explicit conditions, prerequisites, or contrast with alternatives such as get_capability_setup, leaving the agent to infer when cancellation is valid.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate destructive and idempotent behavior; the description reinforces retry safety and adds useful scope safety by emphasizing 'only the exact detached task owned by this server.' This goes beyond the structured data without contradicting it.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single sentence front-loads the operation and packs in the key scoping and idempotency information with no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The output schema and annotations cover result and safety aspects, and the scope note is helpful. The main gap is the missing link between cancel_detached_capture and its siblings start_detached_capture/get_detached_capture for obtaining run_id.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description does not explain run_id beyond implying it identifies the exact task. The parameter name is suggestive, but an agent gets no guidance on format, provenance, or where to find valid values.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a precise verb (cancel), a specific resource (detached task), and a scoping qualifier ('exact...owned by this server'). This clearly differentiates it from sibling tools like start_detached_capture and get_detached_capture.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The intended usage is implied: cancel a detached capture/task. The ownership qualifier provides some exclusion context, but the description does not explicitly tell an agent when to prefer this over alternatives or how to obtain the run_id.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare idempotentHint=true and destructiveHint=false, so the description does not need to restate those. It adds some context by mentioning that cancellation returns 'bounded cleanup state', which gives a behavioral hint beyond the schema. However, it does not explain what changes the cancellation causes or what 'bounded cleanup state' means, and it does not contradict the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence with no wasted words. It captures the action, the target resource, and the return behavior in one concise statement. The term 'bounded cleanup state' is compact, though somewhat specialized, but the structure itself is exemplary.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple: one required parameter, no nested objects, and an output schema is present, so the description need not document return values in detail. The description covers the essential action and the resource scope, and the 'server-owned' qualifier helps an agent distinguish it from related tools. It could mention cancellation semantics or prerequisites, but the low complexity makes the current description sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has only one parameter, operation_id, with no description in the schema, leaving 0% schema coverage. The tool description ties operation_id to a 'server-owned extraction', which gives the parameter semantic meaning beyond its bare name. It does not explain where the operation_id comes from or how it relates to get_extraction, but for a single self-named parameter this is adequate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Request cancellation') and resource ('server-owned extraction'), and even hints at the response ('bounded cleanup state'). It distinguishes itself from sibling cancellation tools like cancel_detached_capture by adding 'server-owned', though it does not name an alternative explicitly. The phrase 'bounded cleanup state' is somewhat vague but does not obscure the core action.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use the tool: to request cancellation of a server-owned extraction. It provides a mild exclusion by specifying 'server-owned', which differentiates it from detached captures or capability setup cancellations, but it does not explicitly name alternatives or state when not to use it. An agent can infer the context, but clearer routing guidance would improve the definition.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already convey that the operation is read-only, idempotent, and non-destructive. The description adds a useful behavioral guarantee: no native artifact content is returned. It also implies a compatibility and ordering constraint on the inputs, which is beyond the structural annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence with no filler. Every phrase contributes: the action, the scope, and a key output constraint. Structurally this is ideal for an agent to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The presence of an output schema and strong annotations reduces the burden, and the simple two-parameter signature lowers complexity. However, the description leaves important context open: what 'compatible' means, what happens when stages are not compatible, and how this tool differs from other comparison tools in the same family. It is minimally viable but not rich enough for fully confident selection.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description needed to clarify baseline_pipeline_id and candidate_pipeline_id. It only obliquely implies they are 'comparable ordered stages'; the roles of 'baseline' versus 'candidate' are left to inference from the parameter names. This is insufficient compensation for the complete lack of schema-level parameter descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific action and resource: compare artifacts pipeline stages. It also adds a distinct boundary ('without returning native artifact content'), which helps set expectations. However, it does not explicitly differentiate from sibling comparison tools such as compare_run_sets or compare_kernel_validation, so sibling differentiation is absent.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is for comparing compatible, ordered stages of artifact pipelines, but it provides no explicit when-to-use guidance, prerequisites, or exclusions. It does not mention alternatives or explain when another comparison tool would be more appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover the safety profile with readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds useful behavioral scoping by emphasizing 'immutable' cohorts and 'exact' correctness metrics, which goes beyond the annotations. No contradiction is present.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single front-loaded sentence that begins with the action verb and contains no filler or redundant phrasing. Every word contributes to the tool's purpose or preconditions.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having an output schema and strong annotations, the tool's request schema contains a complex protocol object that the description never mentions. An agent would struggle to know how to build a request or choose this tool over several closely related comparison and recording siblings.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate, but it only rephrases the notion of two cohorts, loosely mapping to baseline_run_set_id and candidate_run_set_id. It provides no guidance for the optional protocol object, experiment_id, or how to construct a valid comparison request.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a clear verb ('Compare'), a defined resource ('exact correctness metrics'), and a precise scope ('two immutable run cohorts'). This makes the tool's purpose recognizable and distinguishes it from generic run comparison tools, though it does not explicitly name a sibling.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'from two immutable run cohorts' implies a prerequisite about which run sets are valid, and 'exact correctness metrics' gives some sense of when it applies. However, there is no explicit guidance about when to prefer this tool over siblings like compare_run_sets, record_kernel_validation_comparison, or extract_kernel_validation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds a meaningful behavioral trait not in annotations: the operation is a non-persistent preview of compatible frozen cohorts. It doesn't discuss errors or auth, but the annotation coverage lowers the bar.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

One sentence, front-loaded with the core action and the no-persistence caveat, then the routing instruction. No filler or repetition of the schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is sufficient for selecting the tool and understanding its non-persistent nature, and the output schema reduces the need to describe return values. However, given the oneOf request union and many required fields, it leaves the criteria for 'compatible' and how to choose between request variants mostly to the schema. A mid-range score is appropriate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the burden falls on the description, which only contributes the domain constraint 'frozen cohorts.' It does not explain the required baseline/candidate run-set IDs, polarity, practical threshold, metric, unit, or the measurement-vs-runtime-resource union choice. Thus it adds minimal value over the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific action ('Preview') on a specific resource ('compatible frozen cohorts') and explicitly distinguishes itself from record_comparison by noting it does not persist. This is more than a restatement of the tool name and lets an agent tell it apart from the recording sibling.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly frames the intended use as a dry-run/preview and names record_comparison as the tool to use when persistence is wanted. This gives the agent an actionable when/when-not rule, although it doesn't enumerate other comparison siblings like compare_artifact_pipelines.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate this is a mutating, idempotent, non-destructive operation, so the description adds only the 'bounded generic vs replay' boundary. However, the description says 'Create' while the schema allows an operation of 'replace,' and it gives no detail about side effects, update semantics, or how expected_configuration_id is used.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence with no filler or repetition. It is concise and memorable, though its brevity is disproportionate to the complexity of the 19-parameter tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

A configuration tool with 19 parameters, required providers, create/replace operations, provider-specific artifacts, and sibling planning/running tools needs substantially more guidance. The output schema and annotations help, but the description alone does not give an agent enough context to configure a scenario correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 19 parameters and only about 16% schema description coverage, the tool description carries almost no parameter guidance. It does not explain operation create vs replace, provider-specific constraints, request_rate/burstiness relationships, or expected_configuration_id semantics, so an agent is left to infer nearly everything from raw schema properties.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: it creates a bounded generic benchmark scenario, and it explicitly excludes being a provider rollout replay engine. This gives an agent a core sense of what the tool is for, though the language is somewhat jargon-heavy and does not name a sibling tool explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The only usage signal is the negative statement 'not a provider rollout replay engine.' It does not say when to use this tool versus configure_inference_server, plan_inference_scenario, or run_inference_scenario, nor does it explain the difference between creating and replacing a scenario.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations declare idempotentHint=true and destructiveHint=false, and the description adds a significant behavior: the mutation is purely declarative and will not start the server. For a configuration tool this is important context. It also implies via 'create or replace' that an existing declaration may be overwritten, aligning with the idempotent/replace semantics. Minor gap: no explicit note on what happens to a running server if one exists.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence, front-loaded with the action and the critical 'without starting it' boundary. Every word earns its place, and the key differentiator is placed at the end for emphasis.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a declaration-only tool with a rich output schema and fairly descriptive parameter fields, the missing pieces are minor. The description does not mention idempotency/concurrency expectations, but idempotentHint=true covers that. It also does not say when to replace vs create, but the operation enum handles that choice explicitly. Overall, the description is sufficient for an agent to correctly select this tool; the primary gap is not naming when alternative tools are preferred.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 31%, so the description's one clause about not starting the server does little to explain the 13 parameters. However, the schema's own parameter descriptions are fairly rich (mode constraints, workload requirement, base_url loopback requirement, benchmark_python requirement for sglang, expected_configuration_id pattern). The description does not compensate for the uncovered parameters, but the schema already carries most of the semantic weight.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Create or replace' with the specific resource 'local inference-server declaration' and a crucial boundary 'without starting it.' This distinguishes it from tools like configure_workload, run_inference_profile, and configure_inference_scenario which involve active workload or scenario configuration rather than just declaring a server.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The 'without starting it' clause gives implicit guidance that this is a declaration-only tool, but it never explicitly says 'use configure_workload or run_inference_profile when you want to actually start/run things.' The sibling list contains many config/run tools, yet the description does not name any alternative. Usage is implied but not stated as explicit when/when-not guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond annotations, the description adds important behavioral facts: it writes only configuration, preserves existing workloads and experiments, returns the next discovery step, and never executes. This complements the annotations without contradicting them.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded with the most important fact: it writes but does not execute. Every sentence earns its place, covering non-execution, preservation behavior, return value, and update guidance without fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex 13-parameter tool with low schema description coverage, the description gives a good high-level lifecycle but omits guidance on the many optional nested configuration objects and prerequisites. It is adequate for basic selection but not fully complete for invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 15%, so the description must compensate for parameter meaning. It only hints at operation='replace' and the configuration_id parameter, but does not explain the many other complex parameters such as argv, oracle, identity, requirements, or execution_protocol. It also refers to 'current configuration_id' while the actual parameter is 'expected_configuration_id', which could mislead an agent.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Write'), a clear resource ('one validated named workload'), and a scope ('without executing it'). It also distinguishes itself from execution-focused sibling tools by emphasizing that it never executes the command and only writes project workload configuration.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context: call this to write/update a workload configuration, not to execute it. It also provides explicit operation guidance ('Use operation='replace' with the current configuration_id to update an existing workload'). It does not enumerate alternatives or state when-not-to-use relative to sibling tools, so it stops short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already establish that this is not read-only, not idempotent, and not destructive. The description adds the 'durable' trait, implying the created question is persisted and can be retrieved later, which is useful context beyond the structured annotations. It does not disclose other behavioral details such as side effects, required permissions, or lifecycle behavior, but the bar is lower given the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, tight sentence with no filler words; 'durable' and 'diagnostic' both add meaningful context. It is front-loaded and easy to scan, though it sacrifices some completeness for brevity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a creation tool with several closely related siblings, the description is too thin. It lacks usage guidance and parameter semantics, and while an output schema may exist, that does not compensate for missing information about when to create an investigation versus alternative tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate by explaining parameter meanings, but it does not mention question, symptom, or parent_investigation_id. The phrase 'diagnostic question' hints at the main `question` parameter, yet the optional `symptom` and especially `parent_investigation_id` remain semantically unexplained.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific action ('Create') and a clear resource ('a durable diagnostic question'), so an agent can tell this is an investigation-creation tool. It does not explicitly name sibling tools or differentiate itself from other create/record tools like record_hypothesis, but the resource is specific enough to be reasonably clear.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives no guidance on when to use this tool versus alternatives such as list_investigations, get_investigation, or record_hypothesis. There are no exclusions, prerequisites, or hints about when this tool is preferred, leaving the agent to infer usage from the tool name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already mark this as destructive and non-idempotent, but the description adds valuable context beyond them: the token is single-use and the tool executes with side effects. It still does not specify exactly what side effects occur, but the added single-use behavior exceeds what annotations alone provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is one compact sentence that front-loads the action and the side-effect warning. There is no wasted wording, though 'one current plan' is slightly ambiguous.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is an output schema, and annotations cover the destructive/non-idempotent safety profile. However, for a destructive action with undocumented parameters, the description under-specifies exactly what effects occur and what expected_plan_id is used for, making it only minimally complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for the 2 parameters. It gives plan_token a meaningful single-use semantic, but expected_plan_id is never mentioned, leaving its role and relationship to plan_token entirely inferred.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Run') and resource ('one current plan'), and the 'token is single-use, then get_run' wording helps distinguish this one-shot execution tool from sibling capture/planning tools. However, it does not explicitly name or contrast any sibling, so it stops short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'then get_run' implies this is the one-shot execution step followed by run retrieval, and 'single-use' implies the token should not be reused. There is no explicit guidance about when to choose this over start_detached_capture or plan_capture, and no exclusions are stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already flag destructive, open-world, non-idempotent behavior, so the description does not need to restate that. It adds the specific behavior of 'independently revalidate its candidate,' which is useful context, but it does not disclose what side effects occur, what 'lifecycle' entails, or what gets consumed or changed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is one tight, action-first sentence with no filler. It is efficient, but the terseness contributes to ambiguity around domain-specific terms like 'bound reducer lifecycle' and 'candidate,' so it is not a perfect balance of brevity and clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a destructive, open-world, non-idempotent tool, the description is too incomplete: it does not state prerequisites, what makes a reducer 'bound,' what a lifecycle execution actually does, or how this relates to plan_reduction/get_reduction. The presence of an output schema covers return values, but the surrounding execution context is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description is the only place to explain plan_id, yet it never mentions the parameter or how to obtain it. The phrase 'bound reducer' loosely hints at a prior plan object, but the relationship between plan_id and 'bound reducer lifecycle' is left implicit rather than stated.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific action ('execute') and resource ('one bound reducer lifecycle') and adds a behavioral nuance ('independently revalidate its candidate') that goes beyond the bare tool name. However, it does not explicitly differentiate this from closely related siblings like plan_reduction or get_reduction, relying on the term 'bound reducer' to imply the distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to invoke this tool versus alternatives such as plan_reduction, get_reduction, or list_artifact_reductions. The phrase 'bound reducer lifecycle' suggests a prior planning step, but no prerequisites, sequencing, or exclusion conditions are stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

All safety-related annotations are false, so the description must carry the burden of explaining side effects and behavior. It adds 'raw' and 'explicit timing semantics' but does not disclose whether extraction mutates state, requires prior artifacts, or behaves differently across runs; this is a significant transparency gap for a non-readOnly operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single front-loaded sentence with no filler, so it is concise and structurally clean. However, the closing qualifier 'with explicit timing semantics' is vague and could have been used to provide more substantive information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return-value shape is not the main concern, but the description fails to explain prerequisites, what run_id selects, when to prefer this over sibling extractors, or what side effects may occur. In a sibling set this large, the description alone is not enough for confident tool selection.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description does not mention run_id at all. The schema only states that run_id is a required string up to 200 characters, so neither source clarifies what kind of run identifier is expected or how it maps to benchmark samples. The parameter name is mildly self-explanatory, but the description adds no real semantic value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a concrete verb ('extract') and names a specific resource ('raw accelerator benchmark samples'), adding 'explicit timing semantics' as a qualifier. It is reasonably distinguishable at a glance from the many other extract_* siblings, though it does not explain what makes benchmark samples distinct from nvbench or nsight_compute extraction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use this tool versus the large set of sibling extraction tools such as extract_nvbench, extract_observations, or extract_inference_trace. The phrase 'accelerator benchmark samples' implies a benchmarking context, but no prerequisite, exclusion, or selection rule is stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations are all false, so they provide no meaningful signals. The description says 'Extract' which implies a read operation, but it does not disclose whether the tool stores results, modifies state, requires external file access, or has other side effects. With no annotation support, the description carries the full burden but only gives the bare action, omitting any behavioral context beyond the verb.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single short sentence with no wasted words. It front-loads the action and source, making it efficient and easy to scan. There is zero redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with one parameter and an output schema, the description is minimal. It does not explain what 'bounded findings' means, what run_id refers to, or any expected inputs or outcomes. While the output schema might define return structure, the description does not provide enough context for an agent to invoke the tool correctly without external knowledge.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has one parameter, run_id, with zero description coverage. The tool description does not mention run_id at all, leaving the agent to guess what it represents (e.g., a report ID, file path, or run identifier). The description provides no value over the raw schema and fails to compensate for the 0% coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Extract'), a resource ('bounded findings'), and a precise source ('official Compute Sanitizer XML report'). It clearly differentiates this tool from sibling extract_* tools (e.g., extract_pytest, extract_memray) by naming the exact report format. The purpose is unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use this tool versus alternatives, no prerequisites, and no mention of conditions that would make this the right choice. The description only states what it does, leaving the agent to infer when to call it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no positive annotations, the description carries more burden and does communicate that this is an extraction via a public API, suggesting read-oriented behavior. However, it does not disclose possible side effects, whether the extraction is safe to repeat, or what 'bounded' means operationally.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is one concise sentence with no filler and the action is front-loaded. It loses a point because 'bounded execution-path evidence' is dense and could have been expressed more plainly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The output schema may cover return shape, but the description omits the meaning of run_id, any usage conditions, and behavioral caveats. Given one required input and many sibling extractors, this is not enough for an agent to confidently invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The only required parameter, run_id, is completely undocumented in both the schema and the description; schema description coverage is 0%. The phrase 'bounded execution-path evidence' only weakly hints that the run_id bounds the extraction, with no format or source for the ID provided.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('extract') and identifies the source and scope ('coverage.py's public API', 'bounded execution-path evidence'), which separates it from siblings like extract_pytest or extract_memray. It stops short of a 5 because 'bounded execution-path evidence' is jargon and the exact output is left mostly to the output schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies this is the tool for coverage.py-derived evidence by naming coverage.py, but it gives no explicit when-to-use guidance or alternatives. In a large family of extract_* tools, the agent is left to infer the condition for selecting this one.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With all annotations false, the description carries the burden of behavioral disclosure, but it only says 'Extract.' It does not clarify whether the operation is read-only, whether it writes extraction artifacts, whether it requires an existing run, or what side effects may occur. 'Extract' implies retrieval but does not make the behavioral profile explicit.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single dense sentence with no filler and starts with the action verb. It is well-structured and scannable, though its brevity contributes to missing contextual detail.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with two required parameters, no annotation safety profile, and many sibling extractors, the description is too thin. It should clarify run_id semantics, cover all provider enum values, and distinguish itself from similar extraction tools. The output schema helps with return shape but not with selecting or invoking the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for parameter meaning. It adds some semantics for provider values (aiperf maps to requests, vllm_bench maps to aggregate measurements) but says nothing about run_id and omits the sglang_bench provider entirely.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a concrete action ('Extract') and a specific resource ('prompt-free AIPerf requests or vLLM aggregate measurements'), which makes the tool's core purpose identifiable. It is reasonably distinct from sibling extraction tools, though 'prompt-free' and 'inference result' are domain jargon and the sglang_bench provider is omitted.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives no explicit guidance on when to use this tool versus alternatives such as extract_inference_trace, query_measurements, or list_inference_requests. It does not state prerequisites, exclusions, or conditions that would help an agent choose this over a sibling.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations are all false and provide little safety or read-only context, so the description carries the burden. It only says 'extract evidence' and does not disclose what boundedness means, what 'prompt-free' implies, whether there are side effects, or how the evidence is returned. There is no contradiction with the annotations, but the behavioral disclosure is minimal.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is one dense sentence with no filler and the core verb is front-loaded. It is concise, though the heavy use of unexplained jargon slightly reduces accessibility.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With one required parameter and an output schema, the tool is not highly complex, but the description leaves key context undefined: what a 'Mooncake request schedule' is, what 'bounded' and 'prompt-free' mean, and how this tool fits into the extraction workflow. An agent selecting among dozens of sibling tools would need more context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and the description never mentions run_id. The single parameter is a simple string whose meaning is partially inferable from its name and schema title, but the description fails to explain how run_id relates to the extraction, so it does not compensate for the lack of parameter documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb ('Extract') and a distinctive resource ('Mooncake request schedule evidence'), which helps separate it from the many sibling extract_* tools. However, the qualifiers 'bounded' and 'prompt-free' are unexplained jargon, so the purpose is clear but not fully transparent.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given on when to use this tool versus alternatives, and there are no exclusions or contextual cues. An agent is left to guess whether extract_inference_trace is preferred over extract_observations, extract_inference_result, or get_extraction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations are all false, so the description carries the full burden of behavioral disclosure. The description implies a read operation ('Extract') but does not confirm read-only behavior, side effects, permissions, or cost. It also does not clarify what 'bounded' means in terms of output limits or side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (a single sentence) with no fluff, which scores well on brevity. However, it is under-specified—critical information like the parameter semantics and usage context is omitted, so it is not appropriately sized for the missing information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the many sibling extract tools and a single required parameter, the description is incomplete. It does not explain the distinction from other extract tools, clarify 'bounded per-case metrics', or address parameter semantics. The output schema exists but is not shown, so we cannot rely on it to fill gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With a schema description coverage of 0%, the description must compensate, but it does not mention the single parameter 'run_id' at all. It adds no explanation of what run_id refers to, its format, or how it relates to 'kernel-validation evidence', leaving the agent uninformed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Extract'), a resource ('kernel-validation evidence'), and a qualifier ('bounded per-case metrics'), which clearly distinguishes it from other extract_* tools targeting different evidence types. It is not a tautology and communicates a distinct purpose, though 'bounded per-case metrics' is somewhat vague about what exactly is extracted.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus the many sibling extract_* tools or when to avoid it. It does not mention alternatives, exclusions, or prerequisites, leaving the agent to rely on the name and schema alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover idempotency and non-destructiveness; the description adds that the operation is durable and asynchronous by pointing at an operation ID and polling flow. This is useful beyond annotations, though it doesn't spell out resource cost or cleanup.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence, front-loaded verb and resource, with a concrete follow-up instruction. No filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a durable async start command, it supplies the critical next step (poll get_extraction) and notes durability; output schema and annotations cover return and safety profile. The optional threshold semantics remain under-specified, so it is not a perfect 5.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 0% description coverage and the description gives no parameter guidance. run_id and idempotency_key are inferable from names, but temporary_allocation_threshold's meaning and units are left undocumented, so the description fails to compensate for the coverage gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific action ('Start durable Memray extraction') on a specific resource and names the follow-up get_extraction. This clearly distinguishes it from the many extract_* siblings by tool name and operation pattern.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly tells the agent to poll get_extraction with the operation ID after starting, which is clear usage context. It doesn't explicitly state when to prefer extract_memray over other extract_* tools, but the resource-specific name plus visible siblings makes the selection reasonably clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations are all false and provide no safety profile, so the description carries the full burden. 'Bounded evidence' hints at a constrained extraction behavior, but the description does not disclose side effects, whether artifacts are created, required prior steps, or what 'bounded' means in practice.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single concise sentence with no wasted words and front-loads the key action. It is appropriately short for a one-parameter tool, though 'bounded evidence' could use a brief clarifying phrase.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present, return values are covered, but the description lacks sibling differentiation, usage guidance, behavioral details, and parameter context. For a tool in a large family of extract_* tools, this is too sparse to be considered complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and the description does not mention run_id or explain how it maps to the Node/V8 CPU profile. Although the parameter name is fairly self-explanatory, the description adds no meaning beyond what the schema already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description identifies a specific verb ('extract'), a resource ('Node/V8 CPU profile'), and a scope ('bounded evidence'), which distinguishes it from sibling extract_heap_prof by profile type. The term 'bounded evidence' is somewhat jargon-heavy, but the core purpose is understandable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given on when to use this tool versus the many sibling extract_* tools. The resource type implies a context, but there are no explicit alternatives, prerequisites, or exclusions, so an agent must infer selection from the name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

All annotations are false (readOnlyHint=false, idempotentHint=false, destructiveHint=false), so they convey no affirmative safety profile and the description carries the disclosure burden. The qualifier 'bounded' hints at scope limits but never explains what bounds apply, nor whether extraction requires a pre-existing heap dump or produces new artifacts.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single ten-word sentence with no filler, front-loaded with the verb and resource. Every word earns its place; the brevity problem is one of substance, which is penalized under other dimensions, not struture.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Even though an output schema exists and only one parameter is required, essential context is left unexplained: the meaning of 'bounded evidence', the provenance of the heap profile, and how run_id maps to it. Given nearly twenty sibling extract_* tools, an agent needs more to confidently select and call this one.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and the description never mentions run_id, so it does not compensate for the low coverage. The parameter is only partially recoverable because its name and title ('Run Id') are self-evident, but the description does not clarify that run_id selects the run whose heap profile should be extracted.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Extract') and a clear resource ('bounded evidence from a Node/V8 sampling heap profile'). The resource noun distinguishes it from sibling extractors like extract_node_cpu_prof, extract_memray, and extract_perfetto by naming the exact profile type, though it does not explicitly call out that differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus the many sibling extraction tools (extract_node_cpu_prof, extract_memray, extract_perfetto, etc.). There is no when-to-use, when-not-to-use, or alternative routing anywhere in the description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations are all false and carry little signal, so the description bears the burden of behavioral disclosure. It mentions that ncu_report must be installed, which is useful, but it does not disclose whether extraction modifies state, writes artifacts, requires permissions, or returns output in a particular format. No contradiction with annotations occurs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, tightly worded sentence with no filler. It front-loads the core action and the interface used. It is appropriately concise, though it leans slightly under-specified for a tool with no parameter documentation.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given a single required parameter, no parameter descriptions, and annotations that offer no safety or behavioral signal, the description is too minimal. It does not clarify what 'bounded metrics' means, what the expected run_id refers to, what the extraction produces, or how it relates to sibling analysis/extraction tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description does not mention the run_id parameter or explain its meaning. The schema provides the title 'Run Id' and type, but the description adds no guidance on what kind of run is expected or how the ID relates to the ncu_report extraction.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description specifies a concrete action ('Extract'), a resource ('bounded metrics'), and a distinct mechanism ('NVIDIA's installed ncu_report interface'). This clearly identifies the tool's function and separates it from sibling tools like extract_nsight_systems or extract_memray, though it does not explicitly name alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no explicit guidance on when to use this tool versus alternatives. Mentioning 'ncu_report interface' implies an Nsight Compute context, but it does not state when to choose extract_nsight_compute over analyze_nsight_compute or other extract_* tools, nor does it describe exclusions or prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

All annotations are false, so the description carries the full burden of behavioral disclosure. It only says the tool extracts curated evidence; it does not mention whether the tool writes artifacts, mutates workspace state, or has other side effects, nor does it explain what 'curated' means operationally.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with no filler. Every phrase ('extract', 'curated evidence', 'imported official Nsight Systems SQLite export') contributes selection-relevant meaning.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is low-complexity with one required parameter and has an output schema, so the description does not need to detail return values. It covers the source prerequisite and outcome, but it omits run_id semantics and explicit usage boundaries, making it minimally viable rather than fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and the description never mentions run_id. The parameter name and source context imply it identifies the imported run/export, but the description adds no explicit meaning about how run_id is resolved to a specific SQLite export.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('extract'), resource ('Nsight Systems SQLite export'), and expected outcome ('curated evidence'). This clearly distinguishes it from sibling extract tools such as extract_nsight_compute and extract_memray by naming the exact source format.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'imported official Nsight Systems SQLite export' implies the prerequisite and context for use, but it does not explicitly state when to choose this tool over alternatives or when not to use it. The usage is inferable rather than directly guided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With all annotations false, the description carries the behavioral disclosure burden. It does add a useful constraint about the required artifact cardinality and sidecar declarations, which goes beyond a trivial restatement. However, it does not disclose side effects, failure behavior if the artifact constraint is violated, or whether extraction is repeatable, so transparency is partial.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no filler. The first sentence states the exact purpose and source, and the second provides the essential artifact constraint. Every word contributes to operational understanding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter extraction tool with an output schema, the description gives the core input condition and source context, which is helpful. It is incomplete, though, because it does not explain what happens when the artifact cardinality is violated, what output is produced beyond the schema, or how this relates to sibling extraction flows. It is adequate but leaves clear gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for the undocumented run_id parameter. The phrase 'The run must contain...' references the run concept, but it never explicitly states that run_id identifies the preserved bundle to extract from. The single parameter's meaning is left mostly to its title 'Run Id,' with no added semantic guidance in the description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Extract NVBench sample times and frequencies from a preserved bundle.' It clearly identifies the artifact type (NVBench JSON) and distinguishes this from sibling extract_* tools that target other artifacts like pytest, memray, or perfetto. The primary NVBench artifact requirement further narrows the purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a clear precondition: the run must contain exactly one primary NVBench JSON artifact and zero or more declared sidecars. This implies the tool is for extraction from a preserved bundle, but it does not explicitly name alternatives or state exclusions such as 'use import_nvbench if the bundle is not preserved.' Usage context is present but not fully elaborated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

All annotations are false/neutral, so the description must carry the burden of disclosing side effects, idempotency, or whether extraction consumes or deletes observations. It does not; 'bounded' hints at limits but not at behavioral consequences. No contradiction exists, but the behavioral profile is largely unspecified.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence with no filler, the action is front-loaded, and every word contributes to the intended meaning. It is easy to parse quickly, though structural efficiency cannot makeup for missing usage and behavior guidance.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With only one required parameter and an output schema present, the mechanical surface is small, but the description omits usage context, side effects, and sibling differentiation. In a tool list crowded with extract_* alternatives, this is not enough for an agent to confidently select and invoke it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description does not explain how run_id scopes or selects the observations. The parameter's role is left to be inferred from its name and the tool's purpose, which adds little beyond the input schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb ('Extract') and a resource ('bounded semantic observations') with a source ('flameox.sdk'), so an agent can tell this is about SDK-emitted observations raher than generic run data. It distinguishes from the many extract_* siblings by source, though 'bounded semantic observations' remains jargon that is not defined.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given on when to use this tool versus siblings such as extract_pytest, extract_memray, or extract_perfetto. There are no exclusion criteria or alternative names, so the agent must infer when flameox.sdk observations are relevant.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The annotations do not provide positive behavioral hints, so the description carries the burden. It does disclose that the input must be an explicitly registered artifact and that the output is evidence tables, but it does not explain side effects, whether existing evidence is overwritten, or any other mutating behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence with no filler or repetition. Every word contributes to the core meaning, making it highly scannable for an agent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with two parameters, no positive annotation hints, and no schema-level parameter descriptions, this definition is incomplete. The purpose is clear, but an agent lacks enough information to confidently construct the artifact_id/run_id arguments or understand prerequisites.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description does not clarify how run_id and artifact_id relate to the 'explicitly registered OTLP file artifact.' The parameter names are self-explanatory to a degree, but the description fails to explain which registered artifact should be supplied or why artifact_id is optional.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Normalize'), a specific resource ('an explicitly registered OTLP file artifact'), and a specific outcome ('into evidence tables'). It clearly distinguishes this tool from sibling extract_* tools by naming the OTLP artifact type.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use the tool: when there is an explicitly registered OTLP file artifact that needs normalization. It does not explicitly name alternatives or exclusions, but the OTLP-specific scope makes the usage context unambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate readOnlyHint=false and idempotentHint=false, yet the description does not clarify whether the tool returns results directly, creates an extraction record, or requires later retrieval via get_extraction. It adds minor context by saying queries are 'versioned' and the processor is 'local', but it omits any side effects, asynchrony, or setup implications.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single concise sentence and front-loads the core action. The phrasing is economical, though terms like 'versioned curated queries' are jargon-heavy and could be simplified without losing content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with extraction-like behavior and a sibling get_extraction, the description is too sparse to support correct invocation: it does not explain what inputs mean, whether output is synchronous, or how this relates to other extraction and retrieval tools. An output schema exists, so return structure is less of a gap, but overall usability remains weak.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description carried the full burden of explaining run_id and artifact_id. It mentions neither parameter, leaving the agent to infer meaning only from the parameter names and titles 'Run Id' and 'Artifact Id'. This is insufficient, especially for artifact_id's nullable default behavior.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: 'Run versioned curated queries through a configured local Trace Processor.' Combined with the name 'extract_perfetto', it is reasonably clear this extracts Perfetto trace data via a local processor, distinguishing it from extraction tools for other profilers. However, 'versioned curated queries' is somewhat vague about what data is produced.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given about when to use this tool versus sibling extract_* tools or get_extraction. The mention of 'configured local Trace Processor' hints at a prerequisite but does not explicitly explain setup requirements, nor does it name alternatives or exclusion cases.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description does not disclose side effects, whether an extraction artifact is created, permission requirements, or behavior beyond 'Extract'. Annotations do not clarify much: readOnlyHint, openWorldHint, idempotentHint, and destructiveHint are all false, so the agent gains little behavioral certainty from them.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single front-loaded sentence with no filler. Every word contributes meaning, though 'public' is somewhat ambiguous; overall it is appropriately compact for a one-parameter tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The output schema exists, so return values do not need to be described, and run_id is the only required parameter. However, the description leaves the meaning of 'public' unclear and provides no context for selecting pyperf evidence extraction over sibling extractors, making it minimally viable but incomplete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description does not explain run_id beyond the phrase 'pyperf run'. Since run_id is the sole required parameter, the description should at least clarify what value it expects, where it comes from, or how it relates to the evidence being extracted.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Extract'), a specific resource ('public pyperf'), and the kinds of evidence involved ('run, warmup, loop, and value'). This is clear enough to distinguish from sibling extraction tools by name and scope, though it does not explicitly contrast with any alternative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance about when to use this tool versus the many sibling extract_* tools, nor any mention of prerequisites or when not to use it. The phrase 'public pyperf' weakly implies a use case, but the description leaves the decision to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations are all false and provide no safety profile, so the description carries the full burden. It says only what evidence is extracted, not whether the tool writes to an evidence store, whether it is idempotent, whether auth is needed, or whether existing extractions are affected.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with no filler; the evidence categories are compactly enumerated. Every word contributes to defining what the tool extracts.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With one required parameter and an output schema, the description is minimally adequate: it identifies the input kind and the evidence variety. However, it lacks workflow context, preconditions, and side-effect information, so it is not fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and the description does not mention run_id at all. The parameter name is self-descriptive as a run identifier, but the description adds no guidance about where the id comes from, its format, or what conditions must hold for the run to be valid.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb ('Extract') and a concrete resource domain ('pytest') with five concrete evidence categories, so an agent can tell it apart from sibling extract_* tools by domain. It doesn't name the source artifact or a direct sibling, but the pytest scoping makes the purpose clear.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrasing implies this tool is for pytest-specific evidence and is the natural choice when that evidence is needed. However, there is no explicit when-to-use guidance, no preconditions, and no contrast with other extract_* siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

All annotation hints are false and the description adds no behavioral disclosure: it does not say whether extraction mutates state, creates artifacts, requires a prior capture, or is safe to rerun. The description only lists output content, not operational behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence with no filler. Every word contributes to stating what evidence is extracted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, which reduces the need to document return values. However, with dozens of sibling tools, no usage guidance, and no parameter detail, the description is adequate but not fully self-sufficient for an agent deciding whether and how to invoke this tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The sole parameter run_id is required, but schema description coverage is 0% and the description never mentions the parameter or its role. While 'run_id' is somewhat self-explanatory, the description does not add meaning such as scoping evidence to that run or expected value format.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb ('Extract') and resource ('repeated startup, peak RSS, and package-grouped import evidence'), making the core purpose clear. It does not explicitly contrast with sibling extractors like extract_memray or extract_perfetto, so it stops short of full sibling differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance about when to use this tool instead of the many sibling extraction/analysis tools. No prerequisites, exclusions, or decision criteria are stated, so the agent must infer usage entirely from the name and terse description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare read-only, idempotent, and non-destructive behavior, and the description does not contradict them. 'Bounded derived evidence' adds a small signal that results are limited or derived, but pagination behavior, data scoping, or input prerequisites are not disclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single efficient sentence, front-loaded with the verb, and contains no filler. It is under-specified rather than verbose, but the concision itself is good.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool in a long sibling list with four parameters, a one-sentence description is insufficient. It leaves unclear what artifact_id should refer to, how limit/cursor/minimum_repetitions interact, and in what investigation scenario repeated span signatures are relevant. The output schema covers return shape but not usage context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and the description names none of the four parameters. 'Bounded' only vaguely hints at limit/cursor behavior, while artifact_id and minimum_repetitions receive no semantic explanation, so the description does not adequately compensate for the schema gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a concrete action ('Return') and a resource ('repeated span signatures'), so the basic function is present. However, 'bounded derived evidence' is jargon-heavy and does not explain what span signatures represent or how this tool differs from sibling evidence/query tools. It leans on the tool name for disambiguation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given about when to use this tool over alternatives such as get_evidence, query_measurements, or the various extract_* tools. No conditions, exclusions, or prerequisite relationships are stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations are all false, so the description carries the burden of explaining behavioral traits. 'Freeze' suggests a persistent mutation, but the description does not disclose side effects, reversibility, idempotency, or what happens to the cohort or snapshot afterward. This is insufficient for a state-changing tool with no annotation support.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is one short sentence, front-loaded with the key verb, and contains no filler. While sparse, every word earns its place, and the phrasing is memorable and direct.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The input schema is complex, featuring a oneOf request type with included/excluded members, selection objects, and corpus_commit_id, yet the description provides almost no contextual guidance. An agent cannot determine how to choose between run_ids and members, what 'bounded' means technically, or what constitutes a valid freeze operation. The description is far from complete for this complexity level.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% and the description does not mention the request parameter, run_ids, members, selection, or corpus_commit_id. It offers only a high-level metaphor ('bounded cohort', 'one corpus snapshot') that maps vaguely to the request fields but provides no concrete guidance for constructing a valid request.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Freeze') with a clear resource ('bounded cohort') and scope ('against one corpus snapshot'), giving an agent a reasonable high-level sense of the operation. It does not explicitly differentiate the tool from siblings like compare_run_sets or list_runs, but 'freeze' is distinct enough to avoid immediate confusion.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given about when to use this tool versus alternatives, nor any exclusions or prerequisites. The phrase 'bounded cohort' implies a use case, but the description never states when an agent should choose this over other run-set or snapshot-related tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds meaningful behavioral context beyond annotations: results are 'bounded', the URI is 'opaque', and the tool 'never' returns a host path or bytes, which clarifies what callers can and cannot rely on.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence with no filler. It states the core return contract and the key safety exclusion without repeating schema or annotation information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter retrieval tool with rich annotations and an existing output schema, the description covers the essential behavioral contract: what is returned and what is never returned. The main gaps are parameter semantics and explicit sibling routing, but these are minor given the low complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage, and the description does not explain artifact_id's origin, format, or relationship to the returned URI. The parameter name is somewhat self-explanatory, but the description adds no parameter-level meaning, and with zero schema coverage it fails to compensate for that gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description specifies a concrete verb ('Return'), a clear resource ('artifact'), and the exact output shape: 'bounded metadata and an opaque resource URI, never a host path or bytes.' This distinguishes it from related siblings like preview_artifact and get_artifact_pipeline without requiring schema inspection.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use the tool: when you need metadata or an opaque URI rather than raw bytes or a host path. However, it does not explicitly name any sibling alternative or state conditions like 'use preview_artifact for bytes', so the usage guidance remains implicit rather than explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds that the result includes a bounded set of compatible comparison candidates, which is useful behavioral context, but it does not explain how bounds are applied or what criteria determine compatibility.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very short and front-loaded, with no redundant phrasing. However, it is so terse that it omits usage guidance and parameter explanation, making it under-specified rather than optimally concise for the tool's context.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The presence of an output schema and read-only annotations covers return-value structure and safety, but the core concept of 'bounded compatible comparison candidates' remains unexplained. The description also does not position the tool relative to nearby siblings like compare_artifact_pipelines or get_artifact, leaving room for incorrect tool selection.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description never directly mentions pipeline_id or candidate_limit. 'One pipeline' loosely maps to pipeline_id and 'bounded' hints at candidate_limit, but the documentation does not clarify parameter semantics or the meaning of 'compatible comparison candidates.'

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Read') and resource ('one pipeline'), and adds that it also returns 'bounded compatible comparison candidates,' which distinguishes it from list or registration tools. However, it does not explicitly name an alternative or define what makes candidates 'compatible,' so differentiation is mostly implied.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use this tool versus alternatives such as list_artifact_pipelines, get_artifact, or compare_artifact_pipelines. The phrase 'Read one' implies a single-pipeline lookup, but no exclusions, prerequisites, or selection context are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint, idempotentHint, and non-destructive behavior. The description adds useful behavioral context beyond those annotations by emphasizing that the state is durable and persists beyond the original request, which is important for users who need to poll or recover state after a transient request context disappears.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, concise sentence with no filler. It front-loads the core action ('Read durable capability setup state') and places the important temporal condition at the end. Every part of the sentence adds useful information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

This is a low-complexity read operation with one required parameter and an output schema, so the description does not need to explain return values. However, it omits any explicit guidance on how to obtain or identify operation_id, and it does not clarify what 'capability setup state' contains in a way that would fully prepare an agent to invoke it correctly without further inference.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and the single parameter, operation_id, is not mentioned in the description. While the name and title ('Operation Id') make its role partially inferable, the description does not explicitly connect operation_id to the 'original request' mentioned in the text. The description should compensate for the lack of schema-level parameter documentation, but does not do so.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies a specific operation: reading durable capability setup state. It uses a concrete verb ('Read'), names the resource ('durable capability setup state'), and adds a temporal condition ('after the original request disappears') that helps distinguish this from related sibling tools like start_capability_setup, cancel_capability_setup, or list_capabilities.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'after the original request disappears' provides clear temporal context for when this tool is appropriate, distinguishing it as the way to retrieve state when the original asynchronous request context is no longer available. It does not explicitly name alternatives or state when not to use it, but the context is clear enough.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds that the tool exposes parameter and validation metadata, but it does not mention return shape, errors, or whether 'call the matching plan tool' is an action the agent must take rather than something the tool does. There is no contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single short sentence that front-loads the primary action ('Inspect allowed parameters and validation metadata'). However, the second clause 'then call the matching plan tool' is arguably guidance rather than a description of the tool itself, which slightly reduces focus. No word is wasted, but the sentence could be clearer by separating tool function from agent workflow.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has a rich output schema and strong annotations, but the description does not explain what a 'declared workflow' is, what 'allowed parameters' means, or how to map the 'kind' parameter to the corresponding plan tool among many siblings. For an agent facing dozens of planning and extraction tools, this description leaves important routing details implicit.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With schema description coverage at 0%, the description carries the full burden of explaining the 'kind' and 'name' parameters, but it says nothing about them. 'Allowed parameters' plausibly refers to the workflow's own parameters, not the input schema, leaving both required inputs undocumented. There is no hint about valid kind values or how name relates to the declared workflow.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a resource ('allowed parameters and validation metadata') and an action ('inspect'), but it never explicitly states that the tool returns a declared workflow. The 'then call the matching plan tool' clause conflates the tool's function with a downstream agent action, which muddies what the tool itself does. It also fails to distinguish this from the sibling list_declared_workflows.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The instruction to 'call the matching plan tool' implies this tool is used as a precursor to planning, which is a legitimate usage context. However, it does not say when to prefer this over list_declared_workflows or how to select the correct plan tool for a given kind. No exclusions or alternative conditions are given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover the read-only, idempotent, non-destructive nature of the tool. The description adds little behavioral context beyond the resource being accessed; it does not clarify what 'bounded progress' means or how reconnection behaves if the run no longer exists. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single short sentence with no filler, and the key object ('one detached run') is placed at the end. It is appropriately concise, though 'bounded progress' is an unusual phrase that slightly reduces clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter read-only tool with an output schema and strong annotations, the description is mostly complete. The main gap is the unclear meaning of 'bounded progress' and the lack of explicit guidance on when to use this rather than other status/query tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description carries the burden for the single required parameter. The phrase 'for one detached run' implies run_id identifies a detached run, but the description does not explicitly define run_id, its expected format, or its relationship to start_detached_capture. The parameter name is mostly self-explanatory, giving partial credit.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific resource ('one detached run') and the information type ('bounded progress and lifecycle status'), and 'Reconnect' suggests re-attaching to an existing operation. This distinguishes it from sibling tools like start_detached_capture and cancel_detached_capture, though 'bounded progress' is somewhat jargon-heavy.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied: an agent recovering or checking on a detached run would use this tool. However, the description does not explicitly state when to use it instead of related tools like get_run, get_lifecycle_gaps, or the other detached_capture operations, nor does it name alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds that the reference must be 'known' and resolved 'after discovery', which is useful, but it does not disclose behavior such as unknown-reference errors, access requirements, or what the resolved output contains.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single sentence that is front-loaded with the core operation and devotes the rest to the essential calling detail: separate ref_type and ref_id after discovery. Every clause earns its place and there is no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given only two required parameters, an enum in the schema, and an output schema, the call itself is fairly complete. Yet with dozens of sibling get_* tools, the absence of any guidance about when the generic resolver is appropriate versus the specific typed accessors leaves a meaningful selection gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 50%: ref_id is documented, but ref_type is exposed only as an enum without explanation. The description adds the important relationship that ref_type and ref_id are paired and must be passed separately, but it does not clarify what each ref_type value represents or how the reference was obtained during discovery.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific operation ('Resolve a known typed reference') and the key requirement that ref_type and ref_id are passed separately. It is clear that this retrieves an existing referenced evidence object, though it does not explicitly distinguish itself from the many sibling get_* tools or define what 'evidence' means.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

'after discovery' provides a useful usage context: this tool is for resolving references that are already known from a prior discovery step. However, it does not state when to prefer this generic resolver over alternatives like get_artifact or get_run, nor does it give explicit exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the description only needs to add context beyond that. It adds the idea of reconstruction from durable evidence, which implies the result is derived rather than simply fetched, but it does not disclose what happens when evidence is missing or how expensive reconstruction may be.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single front-loaded sentence with no wasted filler and no repetition of schema or annotation details. The phrase 'one bounded experiment outcome' is compact but slightly cryptic, which costs it a perfect score.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With output schema and annotations available, the description does not need to explain return shape or safety, and the core purpose is present. However, the description leaves 'bounded' undefined and does not clarify how an experiment relates to runs, captures, or other evidence-producing tools, making it just adequate rather than complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has one required string parameter with 0% description coverage, and the description does not meaningfully compensate. It only references 'experiment' at the resource level, adding little beyond the schema's own 'Experiment Id' title, so an agent still has to infer what identifier is expected and how to obtain it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb, 'Reconstruct', and a concrete resource, 'one bounded experiment outcome', and adds a mechanism, 'from durable evidence'. This makes its core purpose understandable, though the phrases 'bounded' and 'durable evidence' are jargon-like and do not sharply distinguish it from other get_* tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given about when to use this tool instead of alternatives such as get_run, get_evidence, list_experiment_trials, or get_fault_experiment. There are no exclusions, prerequisites, or conditions that would help an agent decide between this and its many siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already establish readOnlyHint and idempotentHint, and the description does not contradict them. It adds value by disclosing that progress and outcome are durable and can be recovered after a disconnect, which goes beyond the annotation fields. It does not discuss polling or rate limits, but the core behavioral claim is present.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single nine-word sentence with no filler. It front-loads the action and resource, and every word contributes to the meaning.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given a single required parameter, an output schema, and annotations covering safety/idempotency, the description is mostly sufficient for a basic read operation. However, it leaves open how operation_id is obtained and does not distinguish the tool from related extraction or capture status tools, so some context is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has zero description coverage for operation_id, and the description never mentions the parameter or explains where it comes from. The name is somewhat self-explanatory, but the tool description adds no explicit semantic meaning beyond the schema field name.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Read') and names a concrete resource: 'durable extraction progress, outcome, and recovery after disconnect.' This is clearly tied to the get_extraction name and avoids vagueness, though it does not explicitly contrast with sibling getters like get_detached_capture.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'after disconnect' gives clear context for when the tool is useful, but the description does not say when not to use it or point to alternatives such as get_detached_capture or cancel_extraction. Usage routing is left largely to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover readOnly, idempotent, and non-destructive behavior, so the description doesn't need to restate safety. It adds useful context by noting the result is 'immutable' and 'completed', which tells the agent the object is final and only available after completion. This goes beyond what annotations provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, direct sentence with no filler or repetition. It fronts the core action and resource immediately, and every word contributes to the meaning. This is exemplary conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (one parameter), the presence of an output schema, and comprehensive annotations, the description is largely sufficient. It states the operation, the object type, and the state of the result. It lacks only explicit usage context, such as when this should be invoked relative to fault experiment planning or execution, but this is not critical for a straightforward getter.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description must compensate for parameter documentation. It does associate result_id with a fault experiment result, which gives some meaning beyond the raw schema. However, it does not explain where the result_id comes from or how it relates to other tools, leaving some semantic ambiguity for a single required parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Read') with a precise resource ('fault experiment result') and adds state qualifiers ('immutable completed'). This clearly distinguishes it from sibling tools like plan_fault_experiment or run_fault_experiment, which involve creating or executing experiments rather than reading a completed result.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no explicit guidance on when to use this tool versus alternatives. It does not mention that it should be used after running a fault experiment, nor does it name any sibling tools for comparison. The 'completed' qualifier implies a usage condition, but no direct routing or exclusion is given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds that the return value is the current revision with exact evidence edges, which is useful context, but it does not disclose error behavior or revision semantics. There is no annotation contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence with immediate verb-resource-object structure and no filler. Every phrase, including 'current revision' and 'exact evidence edges,' adds meaningful information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple read-only getter with one parameter and an output schema, the description supplies the core retrieval semantics. It lacks usage context and parameter elaboration, but the annotations and output schema cover most of what an agent needs to invoke it safely.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description does not explain finding_id beyond what the parameter name/title already convey. The single parameter is self-descriptive, but the description fails to compensate for missing schema details such as expected format or where the ID originates.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific operation ('Return') and a concrete resource ('the current finding revision'), and adds the distinctive detail 'with its exact evidence edges.' This clearly differentiates get_finding from sibling tools like list_findings or record_finding.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No when-to-use or when-not-to-use guidance is provided, and no alternative tools are mentioned. The word 'current' hints at revision semantics, but the description does not explain when to prefer this over list_findings or how it relates to other finding-related tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already communicate read-only, idempotent, and non-destructive behavior, so the description does not need to repeat those. It adds bounded and source-linked context, but leaves metric and cursor behavior unexplaind. No contradiction with the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single dense sentence with no redundant words. The key action and object are front-loaded, making it easy to scan and understand quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only getter with an output schema and good required-parameter documentation, this is minimally adequate. However, it omits pagination/metric context and does not explain how it fits with related frame-analysis tools, leaving some serach ambiguity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema describes limit, frame_id, and run_or_artifact, but cursor and metric have no meaningful descriptions. The tool's description does not compensate for those gaps - 'bounded' only hints at limit, and 'source-linked' does not explain how metric affects the results.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific verb ('Return') and a specific resource ('direct callees for a frame'), with useful qualifiers ('bounded', 'source-linked') that distinguish its behavior. The noun 'callees' makes the direction opposite to the sibling get_frame_callers evident, so an agent can tell them apart.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given for when to use this tool versus alternatives such as get_frame_callers or get_stack_examples. There is no mention of prerequisites, how the callee relationship is produced, or when this tool should be preferred.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnly, idempotent, and non-destructive behavior. The description adds the qualifiers 'bounded' and 'source-linked', but does not elaborate on pagination behavior, whether the result set is ordered, or how source linking is resolved. It provides useful but minimal added context beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence with no filler words, making it highly concise and easy to parse. However, the term 'source-linked' is used without explanation, slightly reducing clarity for an agent unfamiliar with the internal model.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the 5-parameter schema, partial schema coverage, and a sibling tool get_frame_callees that represents the inverse operation, the description is not fully contextually complete. It leaves the agent to infer pagination, metric semantics, and how direct callers differ from stack examples or trace windows. The existence of an output schema reduces the burden for return values, but the tool's place in a larger workflow remains underspecified.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 60%, and the description adds no parameter-level guidance. While limit, frame_id, and run_or_artifact have some schema descriptions, cursor and metric are undocumented in the schema and unexplained in the description. The description does not compensate for this gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the action ('Return'), resource ('direct callers for a frame'), and key qualifiers ('bounded source-linked'). This distinctly distinguishes it from the sibling tool get_frame_callees, which would return the opposite direction of the call graph.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies its intended use for retrieving callers of a frame, but it does not explicitly state when to prefer this over get_frame_callees, get_stack_examples, or other analysis tools. No alternatives or exclusion conditions are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, and the description's 'Return' wording is consistent with those. The phrase 'current hypothesis revision' adds a small amount of scoping context, but the description does not disclose return behavior, error cases, or versioning semantics beyond what the annotations imply.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single short sentence with no filler or redundancy. It front-loads the core operation and is appropriately sized for a simple read tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter read operation with an output schema and clear safety annotations, the description is minimally adequate. However, it lacks any contextual detail about what a 'hypothesis revision' is, how to obtain a hypothesis_id, or when this call is meaningful relative to recording and using hypotheses.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for explaining the required hypothesis_id parameter. It does not; the description only mentions 'hypothesis revision' and leaves the agent to infer that hypothesis_id identifies the relevant hypothesis. The parameter name alone provides basic meaning, but no additional semantic detail is offered.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Return the current hypothesis revision' clearly states the verb (return), the resource (hypothesis revision), and the operation's read-only nature. It is distinguishable from sibling tools like record_hypothesis, which creates rather than reads, though it does not explicitly contrast itself with any sibling.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives no guidance on when to use this tool versus alternatives such as record_hypothesis or list_findings. It does not state prerequisites, typical call context, or what distinguishes a 'current revision' from other hypothesis-related operations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds minimal behavioral context with the word 'projection,' suggesting a derived or partial view, but does not explain what the projection contains or what happens when no matching investigation exists.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single concise sentence with no filler or redundant restatement. It front-loads the primary operation and resource, and it is appropriately sized for a simple read-by-identifier tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the output schema, the single required parameter, and the read-only annotations, the description is nearly sufficient. The main gap is the undefined meaning of 'current projection,' which could confuse an agent selecting among the many related get_* and investigation tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description does not mention investigation_id at all. The parameter name and title are self-explanatory, but the description does not clarify the expected format, semantics of 'current projection,' or how the ID selects the returned investigation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description says 'Return one current investigation projection,' which clearly identifies a get/read operation on a single investigation resource and distinguishes it from list_investigations and create_investigation. The word 'current' is slightly ambiguous and the description does not explicitly mention lookup by investigation_id, but the tool name and schema parameter make the intent clear.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given about when to use this tool versus alternatives such as list_investigations or get_run. The description does not state any prerequisites, exclusions, or sequencing hints, leaving the agent to infer usage entirely from the tool name and parameter.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare read-only, idempotent, and non-destructive behavior, so the description adds little behavioral context. It only describes the content of the return value (timestamp, identity, missing-parent evidence) rather than any side effects, errors, or performance characteristics. Since the description does not contradict annotations but also does not enhance them, a score of 2 reflects the minimal contribution beyond the structured metadata.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single short sentence, which is technically concise, but it lacks the substance needed to be useful. Under-specification is not true conciseness; the sentence could be expanded substantially without adding fluff. It is not front-loaded with the most important information because there is almost no information at all.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's likely purpose (identifying lifecycle gaps) and the existence of an output schema, the description still does not explain what triggers a gap, how to interpret the evidence, or when to call this function. It omits any context about the lifecycle model or the relationship to artifact_id. The description is incomplete for an agent to confidently invoke the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, meaning the schema provides no description for either parameter. The tool description does not mention artifact_id or limit at all, so the agent receives no guidance on what these parameters mean, their purposes, or how they affect the response. This is a significant gap for a required parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Return') and a resource ('explicit timestamp, identity, and missing-parent evidence'), which conveys a clear action but leaves the meaning of 'lifecycle gaps' ambiguous. It does not distinguish this tool from the many other evidence-returning siblings (e.g., get_evidence, summarise_evidence) beyond the name, so an agent may struggle to know exactly what this tool offers.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. There is no mention of scenarios, exclusions, or conditions that would select this over the many related tools. The agent is left to infer usage solely from the tool name.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.