search_voice_library

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

With no annotations, the description carries the full burden of behavioral disclosure. It warns that long queries 'over-match and return almost nothing' and recommends short queries and filters, which is a key behavioral trait. It also discloses the return structure and how to use results with add_library_voice. However, it doesn't mention rate limits, error handling, or other edge cases, but for a search tool the disclosed behavior is substantial.

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 opening, usage guidance, query-length warning, then organized Args and Returns sections. It is longer than minimal but every section contributes necessary information for 8 parameters and no output schema. Slightly verbose but not wasteful.

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 annotations and no output schema, the description is remarkably complete. It covers the tool's purpose, usage workflow, parameter semantics, return format, and integration with sibling tools like add_library_voice. All 8 parameters are explained, and the return value is described with the important note about public_owner_id + voice_id. No critical gaps remain.

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 names and types with zero descriptions. The description's Args section adds concrete meaning for every parameter, including examples ('raspy', 'gruff'), allowed categories ('professional', 'famous', 'high_quality'), specific use_cases ('characters_animation', 'narrative_story'), and page_size constraints (1-100, default 20). This fully compensates for the schema's lack of 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 clearly states the tool's purpose: 'Search ElevenLabs' shared Voice Library — thousands of ready-made voices.' It distinguishes itself from alternatives by positioning it as 'how you pick a voice instead of dialing one out of parameters,' and connects to downstream tools like add_library_voice and generate_speech.

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?

Explicitly states when to use this tool: as the alternative to manually specifying voice parameters. It provides a clear workflow (search → filter → add → generate) and offers practical advice on query length and filter usage. It also mentions specific example queries and filter values, giving the agent concrete guidance.

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