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

With no annotations, the description carries the behavioral burden. It discloses that it ranks by cost only and does not rank quality, and it explains that it can cover either specified models or the whole catalog. However, it does not describe the output format, whether cache_pct affects estimates beyond a simple adjustment, or any potential side effects or external calls.

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

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

The description is compact and front-loaded with the core purpose. Each sentence adds distinct value: what it does, how inputs are provided, what ranking is produced, and an important limitation. 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 is sufficient to invoke the tool correctly: units, optional parameters, and ranking behavior are all covered. The only notable gap is the lack of an explicit note about the response shape, though the ranking behavior strongly implies it. Given no output schema or annotations, this is a minor omission rather than a critical one.

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

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

Schema description coverage is 0%, so the description must compensate, and it does. It explains input_mtok and output_mtok as monthly volumes in millions of tokens, cache_pct as a 0-100 share of cached input, and models as given model IDs or the whole catalog. All four parameters receive meaningful semantic 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 uses specific verbs and resources: 'Estimate the monthly USD bill of a workload' and 'rank models by cost.' It clearly distinguishes itself from the sibling tools by stating what it does and that it is cost-only, which differentiates it from search_models and get_price.

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

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

The description gives clear input context: monthly token volumes, optional cache percentage, and model selection. It implicitly tells an agent when to use it for cost estimation/ranking, but it does not explicitly name alternatives like get_price for single-model pricing or search_models for discovery, so exclusion guidance is absent.

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 and does well: it discloses return contents (input/output/cached-input prices, verification date, confidence, source URL), the daily re-check process, and data attribution. It does not mention behaviors for invalid model IDs or failure modes, but the disclosed traits are meaningful and beyond the bare function.

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 with zero waste. Core purpose is front-loaded, followed by useful return details and provenance. 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?

For a simple one-parameter lookup, the description covers what is returned, how current the data is, and the source. The main gap is not connecting model_id to the sibling search_models, but overall the tool is adequately specified.

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

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

Schema description coverage is 0%, so the description must compensate. It refers to 'one model' but gives no format, examples, or guidance on where model_id values come from (e.g., search_models). This leaves the sole parameter ambiguous.

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

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

States a specific verb ('Get'), resource ('verified price of one model'), and unit ('USD per million tokens'). Clearly differentiates from siblings by emphasizing 'one model' and 'verified price' rather than searching models or estimating costs.

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

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

The description implies this tool is for fetching a single model's price, but it does not explicitly say when to use it over the sibling tools search_models or estimate_cost. No exclusions or alternative conditions are provided.

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

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

With no annotations, the description carries the behavioral disclosure burden, and it does so well: it states what is searched, what matching models include, and the available filters. It does not mention order, pagination, or result limits, but for a lookup tool this is not a critical omission.

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

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

The description is compact and front-loaded, with the core search purpose in the first sentence, output details in the second, and optional filters in the third. Every sentence adds necessary information with no repetition 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 search tool with no output schema, the description sufficiently covers what is returned and how to use those results with sibling tools. All four parameters are meaningfully described, and the tool's place in the workflow is clear.

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 must helpfully define the parameters, and it does. It explains query as matching by name/id/provider, provider as a slug like 'openai', min_context as a minimum context window, and open_weights as restricting to open-weights-only models.

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 ('Search') and resource ('API Price Index for LLM API endpoints'), and clarifies it searches by name, id, or provider. It also differentiates itself from siblings by noting that the resulting id is meant for use with get_price / estimate_cost.

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 says that returned model ids should be used with get_price / estimate_cost, which guides the agent to the correct downstream tools. It does not formally state when not to use search_models, but the intended workflow and filtering options are clear.

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