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

No annotations are provided, so the description carries the burden. It discloses a key behavioral trait: the generic ranking excludes embeddings, OCR, and realtime models because they price different work, and explains how category adjusts the pool. This adds meaningful context beyond the obvious.

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 front-loaded with purpose, followed by a necessary exclusions caveat and a direct usage statement. No redundant content; every sentence 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 has 4 optional parameters and no output schema. The description covers scope, exclusion behavior, category-specific use, and intended use case, giving an agent enough to select and invoke correctly without additional 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 100%, so baseline is 3. The description adds value by illustrating category usage with 'embedding', clarifying the meaning of the ranking metrics, and noting the default behavior. This goes slightly beyond the schema's dry 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 uses a specific verb ('Find') and resource ('cheapest current models'), and details ranking options (input, output, blended). It also distinguishes itself from siblings by noting the generic ranking excludes embeddings/OCR/realtime and that category can target specific pools.

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 guidance: 'Use to answer "what is the cheapest model for <use case>"' and explains when to pass a category. While it doesn't name alternatives, it gives clear context and exclusions that frame appropriate 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. The description implies a read-only comparison operation with no side effects, but it does not explicitly state that it does not modify data or require special permissions. The behavior is clear given the nature of the tool (comparison), so a slight deduction for missing explicit safety statement.

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, well-structured sentence that front-loads the core action (compare 2–5 models) and provides key details (price, context, capabilities, verdict). No unnecessary words or repetition.

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 only one parameter and no output schema, the description is sufficiently complete. It specifies the input constraints and the output (verdict on cheapest for different workload types). It could briefly mention the output format (e.g., a side-by-side table), but overall it provides adequate context for an AI agent to select and 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?

Schema coverage is 100% and the schema description already explains the parameter (list of 2-5 model ids from search_models). The tool description does not repeat this, which is appropriate. The baseline score of 3 is correct since the schema handles the parameter semantics adequately.

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: compare 2–5 models on price, context window, and capabilities, and provide a verdict on cheapest for input/output/blended. This specific verb+resource combination distinguishes it from siblings like cheapest_models (which likely lists cheapest models without comparison) and get_model_pricing (which focuses on a single model's pricing).

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 context: when you want to compare specific models, with a clear constraint of 2-5 models. However, it does not explicitly state when not to use this tool or mention alternative sibling tools (e.g., cheapest_models for a simple list or get_model_pricing for individual pricing). The mention 'from search_models' provides a prerequisite but lacks 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly enumerates the return fields (input/output/cached price per 1M tokens, blended cost, context window, modality, release date, and page URL) and identifies the id source. It does not mention read-only behavior or error handling, but the verb 'Get' and return-focused wording make the core behavior quite 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 a single, information-dense sentence that front-loads the primary purpose and then concisely enumerates the returned details. Every clause adds value, with no redundancy or 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 one-parameter, read-only pricing lookup tool, this description is nearly complete: it explains what the tool does, what the input should be, where the input comes from, and exactly what data is returned. It lacks an explicit statement about behavior for an invalid or unknown model_id, but that is a minor gap given the overall clarity and lack of an 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?

The schema already provides a complete, well-described parameter (model_id) with examples and the instruction to get ids from search_models, putting schema coverage at 100. The tool description only repeats the id source without adding any additional parameter semantics. 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 clearly states a specific action (get full pricing and capability details), a specific resource (one model by its id), and the id source (from search_models). It distinguishes itself from sibling tools by emphasizing 'one model' and listing detailed pricing/capability fields, which is different from comparing or listing cheapest models.

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 'by its id (from search_models)' provides clear workflow context: the agent should first use search_models to obtain a model id, then use this tool. The singular 'one model' conveys that this is for single-model lookup, implicitly distinguishing from compare_models and cheapest_models, though it does not explicitly enumerate when not to use 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?

No annotations exist, so the description carries full burden. It discloses that the tool lists providers with descriptions and pricing pages, indicating a read-only nature with no destructive 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 well-structured sentence that conveys all necessary information 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 no output schema, the description adequately explains the return values (short description and pricing page), making it complete for a simple listing 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?

With zero parameters, the description adds value by specifying the output includes short descriptions and pricing pages, fully compensating for the lack of a parameter 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 the tool lists all tracked AI model providers with short descriptions and pricing pages, distinguishing it from sibling tools that focus on models and pricing.

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 does not explicitly state when to use this tool versus alternatives, but the context of sibling tools implies it is for provider overviews while others are for model-specific 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?

No annotations are provided, so the description carries full burden. It states the tool searches a 'live' database and returns pricing data, implying read-only behavior. However, it does not explicitly confirm no side effects, rate limits, or other behavioral traits beyond the search functionality.

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: first states the action and data source, second gives two concrete use-case examples. No filler words, well front-loaded, and 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?

Given four parameters, no output schema, and no annotations, the description covers the tool's purpose, return fields, and example queries. It lacks only minor details like default limit or pagination, which are handled by 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?

The input schema has 100% coverage, describing all four parameters. The description does not add extra meaning beyond what the schema provides, so the baseline score 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 clearly states it searches a live LLM pricing database by model name, provider, or id, returning specific fields. It distinguishes itself from siblings like cheapest_models or list_providers by providing explicit use cases: cost queries and provider listings.

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 concrete examples of when to use the tool: 'how much does <model> cost' and 'what models does <provider> offer'. While it doesn't explicitly mention when not to use it or alternatives, the context is clear enough for most agents.

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