search_equities

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

The description adds crucial context beyond annotations: it states the tool reads entirely from a local cache ('equities_master' Tier 1 cache) with no API call, and clarifies the edge case of an empty result when the cache has never been populated. This aligns with annotations like readOnlyHint and idempotentHint, and no contradictions are present.

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 clear one-line summary, usage guidance, technical details, and a dedicated Args section. Every sentence adds value, and the format is easy to scan. The total length is justified by the need to cover parameter details and behavioral nuances, and it remains concise 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 the tool's complexity (1 parameter, no nested objects, has output schema) and the richness of annotations, the description is fully complete. It explains the reverse lookup nature, cache dependency, case-insensitive matching, and the empty list behavior when cache is empty. The output schema likely defines the search result structure, so no additional return-value details are needed.

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 provides only a string type for 'name', but the description's Args section gives detailed semantics: partial or full company name, example values in Japanese and English, case-insensitivity, and matching anywhere in the name. This compensates for the 0% schema description coverage, making the parameter usage crystal clear.

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: searching listed stocks by company name, specifying it's a reverse lookup from name to code. It provides concrete examples in Japanese and English, and the verb 'search' combined with the resource 'equities' leaves no ambiguity. It also distinguishes itself from sibling tools that likely retrieve data by code rather than by name.

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 instructs when to use this tool: when the user knows a company name but not the stock code, with illustrative queries like '住友商事のコードは？'. It mentions case-insensitive partial matching and the source (cache), helping the agent decide when to invoke it over other tools that might fetch data by code or require API calls.

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