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?

With no annotations, the description carries full burden. It mentions side-by-side comparison and verdict but omits details like whether results are real-time, what 'capabilities' includes, or output format.

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

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

Single sentence with no wasted words, front-loading core purpose and result, including range constraint.

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 tool complexity, description is decent but lacks explicit linkage to sibling tools and fails to mention that model_ids come from search_models (though in parameter note). No output schema provided.

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 parameter description explaining model_ids. Tool description adds context about comparison but does not significantly enhance schema explanation.

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

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

The description clearly states the tool compares 2–5 models on price, context window, and capabilities, and gives a cost verdict. It distinguishes from siblings like cheapest_models and get_model_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 use when comparing multiple models but does not explicitly state when not to use or mention alternatives like cheapest_models for a single cost comparison.

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 provided, so description must disclose behavior. 'List' implies read-only, but no explicit mention of no side effects, authentication, or rate limits. Adequate for a simple read operation but lacks full transparency.

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

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

The description is a single sentence that is concise, front-loaded with the action, and contains 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 zero parameters, no output schema, and low complexity, the description is complete. It adequately informs the agent what the tool returns.

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 exist, and schema coverage is 100% (empty). The description adds meaning beyond schema by specifying that the list includes descriptions and pricing pages, which is useful context.

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 'List' and resource 'all tracked AI model providers', with examples (OpenAI, Anthropic, Google) and content details (short description, pricing page). It is distinct from sibling tools which focus on 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 usage context is clear: use when you need a list of providers. No explicit when-not or alternatives mentioned, but the simplicity of the tool makes it implied.

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 description carries full burden. It accurately describes the read-only search behavior and return fields. Could explicitly mention non-destructive nature but generally 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?

Two sentences, front-loaded with action and return, followed by usage examples. 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?

With 4 parameters fully described in schema and return fields listed, description is complete. Could mention default limit but schema handles 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?

Schema coverage is 100%, baseline is 3. Description adds context like 'free-text match' and usage examples, but does not significantly extend 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?

Description clearly states the tool searches a live pricing database by model name, provider, or id, and returns specific fields. It also gives example use cases that distinguish it from siblings like cheapest_models and compare_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?

Provides explicit usage scenarios: answering cost queries or listing models by provider. No explicit when-not-to-use, but the examples are strong guidance.

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