list_models

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

The description covers the basic behavior (listing models/voices, filtering by type) but, given no annotations, it lacks details on authorization, pagination, or output format, which would be helpful for an agent. It is adequate but not comprehensive.

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?

A single sentence that is front-loaded with the key action and resource. Every word is necessary, and it is efficiently structured 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?

For a simple listing tool with one optional parameter and no output schema, the description is fairly complete. It explains the tool's purpose and filtering ability. Minor improvements could include mentioning that voices are for synthesis and models for recognition.

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%, providing full info on the 'type' parameter. The description adds no extra meaning beyond the schema, meeting the baseline for a parameter with an enum.

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 'List' and clearly identifies the resource ('available SaluteSpeech models and voices') and purpose ('for recognition and synthesis'), clearly distinguishing it from siblings like 'recognize_file' which perform actual transcription.

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 usage for listing models and voices, but does not explicitly state when to use it versus alternatives like 'get_task_status' or when not to use it. The context is clear but lacks exclusions or alternatives.

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