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

No annotations exist, so description carries full burden. It details parallel execution, return of responses with model info, latency, and quality scores (if benchmarked). No contradictions.

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 yet complete, with a clear first sentence, use-case paragraph, output format, and parameter list. No wasted 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 complexity (7 params, no annotations, output schema exists), the description covers all necessary aspects: behavior, use case, return format, and parameter details. It is comprehensive.

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?

All 7 parameters (prompt, system_prompt, count, min_tier, provider, max_tokens, temperature) are explained in the description, despite 0% schema coverage. Defaults and purposes are clearly 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 clearly states 'Run the same prompt on multiple models in parallel and return all responses.' It specifies the action, resource, and distinguishes from siblings like 'run' (single model).

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 explicitly says 'Use this for verification and consensus. When accuracy matters more than speed...' and implies when not to use (when speed is priority). This provides clear context and 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?

With no annotations, the description carries the full burden. It discloses the back-translation process, model selection defaults, and return values. However, it does not mention potential failure modes (e.g., model unavailability) or explicitly state read-only behavior, though it is implied.

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-front-loaded with the purpose and process, followed by a structured parameter list. It is slightly verbose but each part adds value; could be tightened slightly without losing 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?

Given 8 parameters (4 required) and an output schema, the description covers the purpose, process, parameters, and return values adequately. No essential details missing for an evaluation 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?

Despite 0% schema description coverage, the description includes a detailed 'Args' section explaining every parameter, including defaults and examples, providing full semantics beyond the schema titles.

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 that the tool evaluates a translation via back-translation and gloss overlap, with a specific verb-resource pairing. It distinguishes from siblings like 'judge' by explaining its unique non-circular metric approach.

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 explains when to use it as a powerful quality metric and provides context for its use, but does not explicitly state when not to use it or mention alternatives beyond implying it differs from other evaluation 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?

With no annotations, the description provides good transparency: it explains bounded concurrency, shared judge pool, incremental file writes, interrupt handling, and resume capability. It adds behavioral context beyond the parameter schema.

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 moderately long but well-structured with a clear opening sentence and a bulleted Args section. Every sentence adds value, though it could be slightly more concise without losing 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?

Given 11 parameters and the presence of an output schema (not shown), the description covers return values (per-item scores, summary statistics, error counts) and incremental write behavior. It is complete enough for an AI agent to understand usage and outcomes.

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 0% description coverage, so the description compensates by explaining each parameter in the Args block (e.g., items structure, rubric dimensions, scale format, default values). This adds meaning beyond the bare 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 it runs judge evaluations at scale on a list of items, distinguishing it from sibling tools like 'judge' (single item evaluation) and 'batch_run' (different purpose). The verb 'run judge evaluations' and resource 'list of items' are specific.

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 explains how to structure items with 'prompt' and optional 'metadata' keys, and details resume behavior for results_file. However, it does not explicitly state when not to use this tool or mention alternatives like 'judge' for single items, though the context of batch vs single can be inferred from sibling names.

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?

No annotations provided, so the description carries the full burden. It discloses key behaviors: bounded concurrency, auto-retry, model selection, incremental results file, and resume support. It does not mention authentication or rate limits, but these are less critical for this tool.

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 a one-line summary followed by context and a structured args list. It is slightly long but every sentence adds value. Could be slightly more concise, but overall well-organized.

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 11 parameters, no annotations, but an output schema exists (so return values are covered elsewhere). The description covers input semantics, behavioral details, and use cases. It may lack edge-case handling, but is sufficient for most batch workloads.

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%, so the description must compensate. It lists all 11 parameters with clear descriptions, including structure for 'prompts', defaults for many, and cross-reference to list_models() for model_id. No parameter is left 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 opens with a clear verb-resource: 'Run multiple prompts in parallel with bounded concurrency and auto-retry.' It lists specific use cases (translation pipelines, data extraction, etc.) and distinguishes from siblings like 'run' (single prompt) and 'batch_judge' (judging).

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 'For batch workloads' and explains auto-retry and resume, giving good context. However, it does not explicitly contrast with alternatives like 'run' or 'batch_judge', though the intent is clear from the use 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?

With no annotations provided, the description carries full burden and discloses key behavior: runs 5 specific challenges, catches hallucination/garbled output, and auto-selects models when no model_id is given. It lacks detail on return format (e.g., whether scores are per-challenge or aggregated) but is otherwise transparent.

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-structured with a lead sentence summarizing purpose, followed by a bulleted Args list. It is slightly verbose (e.g., the second paragraph adds context but could be integrated) but remains efficient 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?

Given the tool's complexity (5 challenge types, auto-selection logic) and the presence of an output schema (though not shown), the description covers the key points. It could mention the output structure more explicitly, but the context signals indicate output schema exists, reducing the burden.

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, so the description compensates by explaining each parameter in the Args section (model_id, provider, min_tier, count). It adds meaning beyond the schema's type/default information, guiding the agent on usage.

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+resource ('Quality-test models') and clearly distinguishes from siblings by detailing the 5 coding challenges (arithmetic, instruction following, etc.). It immediately tells the agent what the tool does and how it differs from other evaluation 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 explains the default auto-selection behavior and optional parameters, implying when to use the tool (for quality testing). However, it does not explicitly state when to avoid using it or compare it to siblings like batch_judge or judge, leaving the agent to infer context.

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 annotations, the description fully carries the burden of transparency. It explains the blind randomization to prevent position bias, the role of multiple judges, and all parameter behaviors (defaults for judge_count, temperature, etc.). No contradictions or hidden 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 well-structured: a clear purpose sentence, mechanism explanation, usage examples, then a bullet-like list of args. It is slightly verbose but front-loaded with essential details, justifying its length.

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 complexity (11 parameters, output schema exists), the description covers all parameters, behavioral nuances, and usage context. No gaps remain for typical use cases, 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 description coverage is 0%, but the description adds thorough meaning to all 11 parameters, including defaults and interpretations (e.g., 'blind: Randomize A/B order per judge to prevent position bias'). This goes far beyond the schema's minimal titles.

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 does 'Blind A/B comparison of two items judged by N models,' specifying the verb, resource, and the unique blind mechanism. It distinguishes well from sibling tools like 'ask', 'judge', and 'batch_judge' by focusing on comparative evaluation.

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 ('for comparing translations, code solutions, summaries, or any pair of outputs') and explains the blind setting. However, it does not mention when not to use it or provide alternatives among siblings, which would improve 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?

No annotations are provided, so the description carries the full burden. It discloses that the key is saved to ~/.model-radar/config.json, but it does not explain whether it overwrites existing keys, validates the key, or what the side effects are. More detail on behavior is needed for safe use.

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 short and front-loaded with the purpose. The list of providers is somewhat lengthy but necessary for parameter clarity. Overall, every sentence serves a function, though the inline list could be trimmed or moved to schema enums for better 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 configuration tool, the description covers the core action and file location. However, it omits what the tool returns (though an output schema exists), error conditions, and whether the key is tested before saving. Given the absence of annotations and the low schema coverage, the description 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 coverage is 0%, so the description must compensate. It describes 'provider' as a key from a listed set and 'api_key' as the value. While the list of providers is helpful, there is no detail on allowed formats, length, or validation for the api_key. This adds moderate meaning beyond the schema's type-only definitions.

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 'Set an API key for a provider' which is a specific verb-resource pair. It also lists the valid providers and mentions the config file location, leaving no ambiguity about the tool's function. Among siblings, no other tool configures keys, so it is well-distinguished.

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 usage for setting an API key, but does not explicitly state when to use this tool versus alternatives (e.g., setup_guide). There is no mention of prerequisites or when not to use it. Usage is clear from the name and context, but guidance is minimal.

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?

Describes pinging providers, returning top N by latency, and the verified parameter behavior (sends real prompt, excludes empty outputs). No annotation contradictions. Could mention rate limits or auth, but sufficient.

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?

Front-loaded with purpose, followed by usage guidance, example, and parameter details. Every sentence is informative. Slightly long but justified by parameter count.

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 output schema exists, description need not explain return values. Covers purpose, usage, parameters, and behavioral details adequately for a tool with 5 parameters and no annotations.

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% (based on context signals), but description provides thorough explanations for all 5 parameters including defaults, meanings, and an example. Adds significant value beyond 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?

Clearly states verb (get), resource (fastest available models), and scope (N fastest, right now). Distinct from siblings like list_models due to focus on speed and recommendation.

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 when to use ('when user wants recommendations or best/fastest/free models'). Includes an example. Does not explicitly mention when not to use or alternatives, but guidance is 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?

With no annotations, the description fully discloses behavioral traits: models are verified alive by default (sends real prompt), provider diversity is enforced (at most 1 per provider), ranking by tier then latency, and return format of model_ids.

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 with a clear structure: summary line, use case context, then detailed parameter explanations. Every sentence adds value without 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?

Given 4 parameters, no required ones, and an output schema, the description is complete. It covers purpose, behavior, parameter details, and return value context, leaving no gaps for an AI agent to misinterpret.

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 add all parameter meaning. The 'Args' section clearly explains each parameter (count, min_tier, free_only, verified) with defaults and semantics, fully compensating for the lack of 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 clearly states the tool's purpose: 'Get N verified-alive models across N distinct providers, ranked by tier then latency.' It specifies the action, resource, and key constraints, distinguishing it from siblings like 'list_models' or 'get_fastest'.

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 identifies common use cases: 'translation pipelines and batch evaluation' and explains the output is ready for 'run' or 'batch_run'. It provides context for when to use the tool, though it stops short of explicitly stating when not to use 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 annotations, the description fully discloses behavior: returns API key storage locations, base_url and model_id (or recommended), and per-app search paths for specific apps on multiple OS. It explains how the host can use this info, providing complete transparency about what the tool does and its output.

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-structured: first line states purpose, then 'Returns:' lists outputs, then 'Args:' details parameters. Every sentence adds value without redundancy, fitting the tool's complexity into a compact, readable format.

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 complexity (multiple outputs, OS-specific paths, parameter interactions), the description covers all necessary information. The output schema exists but description already explains return values, making it complete for agent usage.

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%, so the description compensates fully: it explains each parameter's purpose, defaults, and behavior (e.g., 'Omit to get a recommended model at min_tier'). This adds substantial meaning beyond the bare schema types and 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 clearly states the tool's purpose: 'Tell the host agent where to search the machine to swap in a model-radar model.' It specifies the verb ('tell'), the resource ('model-radar model'), and scope (search locations, API keys, base_url, model_id). This distinguishes it from sibling tools like list_models or configure_key.

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 explains when to use the tool and how to use arguments: omit model_id for a recommended model, use provider to limit, and min_tier default. It doesn't explicitly state when not to use or provide alternatives, but the context is clear for this specialized tool.

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?

Given no annotations, the description carries the full burden. It discloses auto-selection of diverse judges, structured output enforcement, retry logic, and inter-rater agreement computation. It does not mention destructive actions (likely none) or rate limits, but these are not critical for a query tool. The description is informative enough.

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: it starts with a clear purpose sentence, then lists features, then usage, then parameter details. Every sentence adds value with no redundancy or fluff. It is appropriately sized for a tool with 9 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?

The tool has an output schema (not shown) but the description mentions aggregate scores and inter-rater agreement metrics, giving a good idea of output. With 9 parameters and 2 required, the description covers all essential aspects: purpose, features, usage, and parameter details. It is complete for an evaluation 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 has 0% description coverage, but the tool description explains all parameters: prompt, rubric, scale, count, min_tier, free_only, output_format, max_tokens, temperature. It adds meaning beyond the schema by describing their purpose and defaults. This fully compensates for the lack of 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?

Clearly states the tool rates a single item using multiple judge models and returns aggregate scores. The verb 'rate' and the resource 'single item using judge models' are specific. Distinguishes from siblings like 'batch_judge' (likely for multiple items) and 'compare' (likely for comparing two items).

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 'Use this for evaluation tasks: rating translations, code quality, content accuracy, or any rubric-based assessment.' This provides clear when-to-use guidance. However, it does not mention when not to use or direct alternatives, so it loses a point for not excluding sibling tools like 'batch_judge' for multi-item scenarios.

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 important behavioral trait 'without pinging' and explains output fields (model_id, label). No annotations exist, so the description carries the burden. It does not mention side effects, but it's a read 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?

Concise yet comprehensive: one sentence for purpose, one for response structure, and bulleted parameter descriptions. No wasted 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 output schema exists and the description explains output fields, the tool is fully specified for its purpose. Covers what it does, when to use, parameters, and response.

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 coverage, the description provides detailed explanations for all 4 parameters, including valid values for tier, examples for min_tier, and meaning of free_only. This fully compensates for the lack of 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 clearly states 'List models in the catalog' and explicitly says when to use it: 'Use when the user asks what models are available or to browse by tier/provider/free.' It distinguishes itself from siblings by focusing on listing without pinging.

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?

Provides explicit usage context ('Use when...'), but does not mention when not to use it or compare directly to alternatives like get_fastest or scan. Still, the guidance is clear and actionable.

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 annotations, description carries full burden. It discloses 'No network requests — instant response', indicating it's fast and non-blocking. Does not mention auth or read-only nature explicitly, but the context implies it's safe.

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 fluff. Front-loaded with key information about what it lists and how many. 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?

For a no-parameter tool with an output schema, the description is complete. It tells what the tool does, how to use it, and a behavioral trait. No 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?

No parameters, so baseline 4. Description adds meaning by explaining what the output includes (status fields), which the schema alone might not convey fully.

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?

Description explicitly states it lists all 21 providers with their status (configured/unconfigured, enabled/disabled, model count). The verb 'list' and resource 'providers' are clear. It also distinguishes from sibling tool 'provider_status' by being an overview.

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?

Directly advises to 'call this first' to see which providers have API keys, providing a clear usage context. Does not explicitly state when not to use, but the guidance is helpful.

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 the tool's behavior: pinging one model per provider and returning latency/status. Since no annotations are provided, the description carries the full burden. It does not mention whether the operation is read-only or any side effects, but the behavior is adequately described for a health check tool.

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 long, front-loaded with the main action, and every sentence adds value. There is no wasted text.

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 has no parameters and an output schema exists (not shown), the description explains the return value (latency and status) and purpose. It is complete for the tool's 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?

There are no parameters, and schema coverage is 100% (empty). The description does not need to add parameter semantics, so a baseline of 4 is appropriate.

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 'Check health', specifies the resource 'all configured providers', and explains the method 'pinging one model from each'. It distinguishes itself from sibling tools by being a diagnostic health check rather than a model invocation or configuration tool.

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 usage context: 'Useful for diagnosing which providers are currently responsive vs overloaded.' However, it does not explicitly state when not to use this tool or name alternative tools for related tasks.

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 annotations, the description carries the full burden. It discloses that previous model lists are discarded and replaced (destructive behavior), that only providers with API keys are fetched, and that other providers keep their existing list. It also mentions the optional ping test, but does not cover all potential side effects like rate limits or network latency.

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 main purpose, followed by details and Args. It is well-structured and not overly verbose, though it could be slightly more concise by merging some sentences.

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 exists (so return values are handled), the description covers all necessary aspects: what the tool does, when to use it, parameter details, and optional post-refresh actions. It is complete for the tool's 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?

Schema coverage is 0%, so the description must compensate. It includes an explicit 'Args' section that explains each parameter in plain language: provider (optional), run_ping (triggers ping test), ping_limit (max models to ping). This adds significant meaning beyond the schema's type/name only.

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 'fetch and replace' and the resource 'model lists from configured providers' with specific examples (openrouter, nvidia, groq). It distinguishes from siblings like list_models (which likely just lists) and scan/get_fastest (which are for discovery after refresh).

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 guidance: 'Only providers with API keys are fetched' and 'Use this to get the current catalog, then call scan() or get_fastest() for discovery.' It implies when to use but does not explicitly state when not to use or provide 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?

No annotations are provided, so the description carries full burden. It discloses that the server exits with code 0, that the process manager starts a fresh process to load updated code/tools, and that the client must reconnect. It also mentions the environment variable to disable restart. This is comprehensive for a tool with no 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 with two short paragraphs. The first sentence immediately states the core purpose, and subsequent sentences add necessary detail without redundancy. Every sentence adds 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?

Despite having no parameters, the description fully covers the restart behavior, reconnection requirement, and configuration option. With an output schema present, the description is not required to explain return values, and it would add no extra utility here.

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 parameters, so baseline is 4 per rubric. The description does not discuss parameters because there are none, which is appropriate. No additional parameter information is needed.

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's action: 'Request the server to exit so a process manager can restart it (SSE only).' This specifies a unique verb ('restart') and resource ('server'), and the SSE-only qualifier distinguishes it from sibling tools that perform other actions.

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 explains when to use the tool: with SSE and when a process manager is in place. It also notes that restart is allowed by default and can be disabled. However, it does not explicitly state when not to use or mention alternative tools, but given the specific context, the guidance 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?

No annotations were provided, so the description carries the full burden. It covers auto-selection of fastest model with fallback, free_only filtering, min_tier, max_tokens, and temperature. It does not discuss costing or rate limits, but the core behaviors are well 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 somewhat lengthy due to the Arg list, but it is well-structured with clear sections. The main purpose is front-loaded. No unnecessary sentences.

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 8 parameters, 1 required, and an output schema, the description covers all parameters, explains the auto-selection logic, and mentions the response. It is complete for an AI agent to use.

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 description includes an 'Args' section that explains each parameter (prompt, system_prompt, model_id, provider, min_tier, free_only, max_tokens, temperature) beyond the schema titles. With 0% schema description coverage, this is essential and well executed.

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 'Run a prompt on the fastest available model and return the response.' This is a specific verb+resource, and it distinguishes from siblings like batch_run or judge by emphasizing fastest auto-selection.

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 says 'Use when the user wants to execute a prompt.' and explains options for controlling model selection. It implicitly guides when to use alternatives like list_models(). However, it does not explicitly state when not to use this tool.

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 annotations, the description fully explains the ping-and-sort behavior, verification logic, caching, and what BROKEN status means. It lacks details on error handling or rate limits, but covers core behavior well.

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-structured with a lead-in, detailed verification note, and bulleted args. It is somewhat lengthy but every sentence adds value; could be slightly more concise.

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 8 parameters and existence of an output schema, the description covers the core functionality, return order, verification behavior, and caching. It does not explain the output structure beyond 'fastest-first', but the output schema likely covers that.

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 description provides comprehensive explanations for all 8 parameters, including examples and default values, compensating for the 0% schema description coverage. Each parameter's purpose and valid values are clearly 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 clearly states 'Ping models in parallel and return ranked results by latency' with a specific verb and resource. It also distinguishes from siblings like 'get_fastest' by mentioning 'ranked list' and 'Use when you need live speed data or a ranked list.'

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 says 'Use when you need live speed data or a ranked list' and notes the time range 'Takes 2-10 seconds depending on filters.' It does not explicitly mention when not to use or alternatives like 'get_fastest', but the context is 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?

No annotations are provided, so the description carries full burden. It discloses that started_at is set at server start and uptime_seconds is elapsed seconds. As a read-only operation with no side effects, the description is transparent enough for safe invocation.

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 sentences, each essential: first states core purpose, second provides usage examples, third clarifies the meaning of returned fields. No fluff, front-loaded.

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 zero parameters and presence of an output schema (mentioned in context signals), the description adequately explains the return values (started_at, uptime_seconds) and their meaning. No additional information is needed for a tool of this simplicity.

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 parameters, with 100% schema description coverage. Per guidelines, 0 parameters yields a baseline of 4. The description does not need to add parameter information as none exist.

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?

Description clearly states it returns server process start time and uptime, with specific examples ('how fast did model-radar come up'). The verb 'Return' and resource 'server process started/uptime' are specific, and no sibling tool duplicates this function.

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 usage directions are given ('Use to answer questions like...'), providing clear context for when to invoke. However, it does not mention when not to use or list alternatives, but given the uniqueness of the tool and simplicity, this is sufficient.

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 annotations present, the description carries full burden. It describes the tool's read-only nature, its prioritized list output, and the optional argument behavior. It does not mention error handling (e.g., invalid provider) but is otherwise transparent about what the tool does and returns.

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-structured: a concise one-sentence summary, followed by a paragraph detailing behavior, a use case sentence, and an Args section. Every sentence adds value without redundancy. The most critical information is front-loaded, and the format is easy to parse.

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 existence of an output schema (covering return structure), the description adequately covers all necessary context: purpose, when to use, parameter semantics, and behavioral modes. There are no missing pieces for an agent to correctly select and 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 schema has 0% description coverage, so the description must compensate. It does so by clearly explaining the 'provider' parameter: 'Specific provider key to get instructions for (optional). Omit to see all unconfigured providers.' This adds essential meaning beyond the schema's type-only definition.

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 clear verb-resource pair ('Get setup instructions for adding free model providers') and distinguishes itself from siblings like configure_key and list_providers by focusing on setup instructions. It also explains the two behavioral modes (with/without provider argument), making the purpose unmistakable.

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 use context ('help your user expand their model coverage') and explains the two invocation modes. However, it does not explicitly state when not to use this tool or name alternatives, though sibling tools imply the boundaries. The guidance is clear but lacks exclusionary language.

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?

No annotations are provided, so the description carries full burden. It explains the step-by-step behavior and that the tool prompts the user, but does not disclose potential side effects, idempotency, or whether state changes persist.

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-structured with bulleted steps and a clear purpose upfront. It is slightly verbose but each sentence adds value, and the parameter descriptions are integrated logically.

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 complexity as a multi-step workflow, the description covers all steps, parameter behavior, and user interaction. An output schema exists, so explaining return values is unnecessary. It is sufficiently complete for an AI agent.

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 compensates by explaining the meaning of 'step' (1-4) and 'provider_selection' (list from step 2). It adds context not present in the schema, such as the mapping of steps to actions.

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 tool as a deterministic setup workflow for getting API keys installed, with explicit steps. It distinguishes from sibling tools like 'configure_key' by presenting a multi-step interactive guide.

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 specifies that steps must be run in order and details each step's purpose. It explains how to handle the provider_selection parameter when omitted at step 3, but does not explicitly state when not to use this tool.

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