Recommend Typecast voices from a text description

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

Although annotations are not provided, the description compensates by stating the exact return fields (voice_id, voice_name, score), that results are sorted by relevance, and the count parameter range. It also notes that the tool returns limited information, setting appropriate expectations. No contradictions with annotations (since none exist).

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 brief explanation of the limited return value, then clear parameter descriptions, and a returns clause. Every sentence adds value, and it is not verbose.

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 that there is an output schema (though not detailed in the prompt) and no complex nested objects, the description covers the tool's functionality fully: input, output, post-recommendation steps, and parameter constraints. It addresses the need for further metadata retrieval, making the context 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 description coverage is 0%, but the description adds complete parameter documentation: 'query' is described as 'Text description of the desired style, mood, language, use case, or content context', and 'count' as 'Maximum number of recommendations to return. Must be 1-10.' This fully compensates for the schema gap and adds essential meaning.

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 'Recommend' and the resource 'voices that match a natural-language text description'. It distinguishes from sibling tools like get_voices and get_voice, which retrieve metadata, by specifying that this tool only returns voice_id, voice_name, and score. The purpose is precise and unambiguous.

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 tells when to use this tool: to find voices from a text description. It also provides clear guidance on what to do next: call get_voices or get_voice with the returned IDs for full metadata before making TTS requests. This explicit 'when-to-use' and 'alternatives' guidance is excellent.

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