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

Annotations already declare readOnlyHint=false and destructiveHint=false. The description adds the 'append' semantics but does not disclose further behavioral traits like potential side effects (e.g., notifications, permanence of comments) or prerequisites. It is consistent with annotations but adds only modest behavioral context beyond 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 exceptionally concise, using two sentences to establish purpose and use cases, followed by a compact args line. Every sentence earns its place with no redundant 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 tool is simple, an output schema exists, and the description covers purpose and usage adequately. It does not mention idempotency (repeated calls create multiple comments) or error cases, but these are not essential given the low complexity and available structured information.

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 each parameter having clear descriptions and examples. The description's 'Args: id, author, body (markdown ok)' simply repeats schema information without adding new meaning, so it adds no value beyond the structured 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 clearly states a specific action ('Append a comment') and resource ('feedback item's discussion trail'), which distinguishes it from sibling tools like update_feedback or update_status. It is unambiguous and immediately 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?

The description provides concrete use cases ('investigation notes, questions back to the reporter, or reasoning worth preserving'), giving clear context for when to use the tool. However, it does not explicitly mention when not to use it or name alternative tools, so it falls 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?

Beyond the annotations, the description discloses the atomic claim behavior, the exact state changes (assignee_agent set, status moves to 'in_progress'), and the failure condition with a message naming the holder. It also explains the semantics of force=true. This is rich behavioral context that annotations alone do not 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 three short paragraphs, with the key action in the first sentence. Every sentence adds necessary detail: the purpose, the parameters, and the outcome/failure. There is no redundant or filler text; it is appropriately sized for the tool's complexity.

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 covers what the tool does, when to use it, parameter semantics, success state changes, and failure behavior. Given that an output schema exists (though not shown), the description need not detail return values. For a claim tool with concurrency concerns, this is complete and self-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?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the parameter roles in context (agent as 'your name', force with its default and implication), and by tying them to the success/failure behavior. This exceeds the schema's plain descriptions, justifying a 4.

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+resource: 'Atomically claim a feedback item for an agent before working on it.' This clearly distinguishes the tool from siblings like update or resolve, and the atomicity detail adds unique purpose. The wording prevents any ambiguity about what operation is performed.

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 states when to use it ('before working on it') and explains the failure mode and force option, giving practical usage guidance. It does not explicitly name alternative tools for similar tasks, but the context makes the appropriate scenario obvious. The guidance is clear enough to be effectively 4.

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 clear. The description adds valuable behavioral context by enumerating the full content (description, repro steps, console lines, network entries, links, linked change, comment trail) and advises reading it before a fix. This enriches the agent's understanding beyond 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 concise and well-structured: a one-sentence purpose with a detailed list of contents, a brief args line, and a final usage directive. Every sentence earns its place, and it is front-loaded with the core purpose and immediate value.

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, annotations covering safety, and a description that explains purpose, contents, and usage, the description is fully complete for a GET tool. It tells the agent exactly what to expect and how to use it, leaving no significant 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 100%, and the description repeats the two parameters (id, response_format) with brief format notes (fb_..., markdown|json). The schema already provides examples and defaults, so the description adds minimal additional semantic value beyond reinforcing the id format. Baseline 3 is appropriate given full schema 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 uses a specific verb ('Fetch'), a clear resource ('one feedback item'), and enumerates the full context returned, distinguishing it from sibling tools like loopback_list_feedback (which lists items) and loopback_get_stats (which returns stats). It clearly communicates the tool's scope and uniqueness.

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 explicit usage context: 'Read this before starting a fix' and states it contains everything captured at report time. This clearly signals when to use, though it does not explicitly name alternatives or when-not-to-use scenarios. It gives strong situational guidance without directly comparing to 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 declare readOnlyHint, idempotentHint, and destructiveHint. The description adds return format details, which is helpful beyond 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?

Three concise sentences, front-loaded with purpose, no wasted words. Efficiently communicates usage, parameter, and return structure.

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 stats tool with one optional parameter and clear output described, the description is fully complete, aided by annotations and sibling 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 covers the parameter completely. The description restates the parameter's purpose without adding new semantics, meeting the baseline for 100% 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 clearly states it provides an overview of the feedback queue with counts by project and status, distinguishing it from action-oriented siblings like loopback_submit_feedback.

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 says 'Use to orient before picking work, or to report queue health,' giving clear context for when to use this tool versus 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?

Discloses the write operation and merge behavior ('Provided fields merge into existing links') and clarifies a non-destructive update with idempotentHint true. This goes beyond annotations by revealing the upsert semantics and its place in the audit flow. No 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?

Three concise sentences cover purpose, args, and invocation timing. No fluff. The second paragraph lists args efficiently and the third gives a crisp trigger condition.

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 small, well-annotated tool with a complete schema and output schema, the description supplies the essential workflow placement and merge semantics. Nothing needed beyond what's given; the output schema exists, so return-value detail isn't required.

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 already covers all parameters with examples and 100% coverage. The description adds the merge behavior, telling the agent that partial field updates are allowed and that fields combine with existing links. That contextualizes usage beyond schema 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 opens with 'Attach the fix to a feedback item so the loop is auditable' – a specific verb-target-purpose. It names the exact fields (repo, branch, commit, pr_url, diff_summary) and differentiates from sibling tools like loopback_update_status by defining the unique link-record operation.

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 states when to call: 'right after committing the fix, before updating status to fixed'. Provides clear workflow context, implying this tool precedes status updates. No explicit alternatives, but the timing instruction is strong 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?

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds meaningful behavioral details: results are 'ordered most-severe first, then newest' and pagination metadata includes total, count, offset, has_more, next_offset. No contradictions 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 structured into clear segments: purpose, args list, return behavior, and typical flow. It is slightly longer than necessary due to repeating schema parameter details, but each section earns its place and there is no 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?

The description is highly complete: it explains the tool's role, all major filtering dimensions, ordering and pagination behavior, and provides a concrete workflow example. Output schema exists, but the description adds practical context that helps an agent decide when 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?

Schema description coverage is 100%, so the baseline is 3. The description summarizes the parameters with defaults and enum values, but does not add any semantic meaning beyond what the schema already provides for each 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 'List feedback items, filtered and paginated' with a specific verb and resource, and distinguishes itself from siblings by positioning as 'the entry point for work the queue'. This clearly separates it from tools like loopback_get_feedback or loopback_update_status.

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?

Gives explicit usage context as the entry point for the queue and provides a typical agent flow: list → claim → fix → link → update status. However, it does not explicitly state when not to use it or mention alternatives, 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?

The description adds meaningful context beyond the annotations: it's a write that 'turns the reporter's pin full green' and explains the consequence of omitting the agent parameter. It also clarifies the semantics of 'verified' and 'wontfix'. Annotations already indicate a non-read-only, idempotent write, and the description enriches this without 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 concise and well-structured: purpose first, then parameter explanations, then a usage guideline. Every sentence adds essential information, with no filler or 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?

The description covers purpose, parameters, usage guidance, and side effects (turning pin green). The output schema exists (though not shown) so return values are handled. Minor gaps: it doesn't state whether a closed item can be reopened or how this affects statistics, but these are not critical for correct 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 coverage is 100%, so parameters are well-defined. The description adds extra nuance for 'agent' (records generic default if omitted) and reinforces the meaning of the resolution enum, providing value 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 opens with a specific verb+resource ('Close a feedback item with a final outcome') that clearly distinguishes this from siblings like loopback_update_feedback or loopback_update_status. The notion of a 'final outcome' (verified/wontfix) uniquely identifies the tool's role.

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?

Explicit guidance is given: 'Prefer verifying before resolving' and re-checking the UI or metrics before calling. This implies when to use the tool (after verification) and sets a behavioral expectation, though it doesn't explicitly state alternative tools for non-final updates.

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 carry no useful hints (all false), so the description carries the burden. It discloses that new items start with status 'open', returns a JSON with generated id, and mentions tagging to a project. It does not mention permissions or idempotency, but for a create operation the main side effect is explicit from 'Create'.

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 front-loaded with the core action, then follows with use case, parameter list, return behavior, and an example. Each section earns its place without padding; the example is especially valuable and the overall length is appropriate for a 16-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?

Given the complexity (nested objects, 16 params) and that an output schema exists, the description still adds necessary context: usage scenarios, parameter listing, return format, and a concrete example. It does not explicitly discuss idempotency or failure modes, but these are partially covered by annotations and the 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?

Schema coverage is 100%, giving a baseline of 3. The description adds a concise parameter summary and a concrete example ('Pay button dead on iOS Safari' with project='shop-web', type='ui', severity='p1', route='/checkout') that clarifies how to combine parameters and the expected value formats.

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 'Create a new feedback item in the Loopback bus', clearly using a specific verb and resource, and differentiates from siblings by focusing on creation ('file any observation'). The sentence 'Every item is tagged to a project' adds scope specificity beyond the tool name/title.

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?

It provides a clear use case ('Use this to file any observation about a running product') and enumerates feedback types, but does not explicitly name alternatives or state when not to use it (e.g., existing items should be updated rather than re-submitted). The context is clear enough to avoid obvious misuse.

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 discloses that every change is recorded as a comment naming old and new values, that nothing is silently rewritten, and that omitted fields are left alone. It also explains the author parameter's role in the audit trail. These go beyond the annotations, which only state readOnly=false, destructive=false, idempotent=true.

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 well-organized into purpose, usage, args, and behavior, but it is slightly longer than necessary. The rationale sentence ('Feedback is filed fast and imprecisely on purpose') is contextual but not essential. Still, every sentence earns its place and the front-loading is excellent.

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 100% parameter coverage, the description only needed to add use-case clarity and behavioral notes, which it does thoroughly. It covers correction semantics, audit trail, partial updates, and sibling tool differentiation, making it complete for an agent to invoke 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%, so the baseline is 3. The description adds extra meaning by summarizing the args, explaining the author default ('use your agent name so the trail says who re-triaged it'), and clarifying that fields can be omitted and left unchanged. This adds genuine value 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 opens with 'Correct an item after it was filed' and enumerates the editable fields (title, body, severity, type, project, route), making the verb and resource explicit. It also distinguishes itself from siblings like loopback_update_status (progress) and loopback_link_change (fix) by explicitly stating 'This is for correcting the REPORT.'

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?

It gives concrete when-to-use examples ('re-rank a severity you now understand, fix a mis-guessed type, correct a typo, or move an item to the right project') and explicitly names the alternatives: 'To record progress use loopback_update_status, and to record the fix use loopback_link_change.' This fully clarifies use vs 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=false, destructiveHint=false, and idempotentHint=true. The description adds valuable behavioral context: the note is recorded as a comment for the audit trail, the author defaults to 'agent', and status changes follow a defined workflow. 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 three concise, front-loaded sentences. The first sentence immediately states the core action, the second lists arguments compactly, and the third gives targeted guidance for when to use this tool versus the resolve sibling. No unnecessary words.

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 rich schema, clear annotations, and presence of an output schema, the description fully covers what an agent needs to select and invoke the tool. It explains the workflow, audit trail behavior, and how to avoid misusing it for final closure.

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 each parameter fully. The description adds minor usage nuance (e.g., 'Use fixed after making the change') but largely repeats schema-provided information about the note and author defaults.

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 ('Move a feedback item through the workflow') and enumerates the exact state transitions. It clearly differentiates from the sibling 'loopback_resolve_feedback' by reserving that tool for final verified/wontfix closure.

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: it maps the workflow order, directs to use 'fixed' after making the change, and names loopback_resolve_feedback as the tool for final closure. It does not explicitly exclude other sibling tools like loopback_update_feedback, but the workflow framing gives enough guidance for most cases.

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