list_speech_models

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

No annotations are provided, so the description carries the full burden. It discloses the exact output scope ('Returns id, name and pricing only') and implies a read-only list operation, but it does not explicitly state whether it is read-only, whether authentication is required, or any rate limits. The note about pointing the environment variable is useful context, but key behavioral traits are left implicit.

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 two sentences, front-loaded with the core purpose and immediately followed by return details and a practical use case. No wasted words; 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?

Given zero parameters and an existing output schema, the description is complete for a simple list tool. It covers the purpose, return scope, and a usage scenario. It does not mention prerequisites like authentication, but that is minor and not critical for this zero-parameter, read-oriented tool.

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 tool has zero parameters, so the schema already covers everything. The description need not explain parameters; the baseline for no params is 4. It does not add parameter-specific meaning, but none is needed.

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 states a specific verb ('List') and a precise resource filter ('OpenRouter models whose output modality is speech'). It clearly distinguishes from siblings like render_voiceover or model_info by focusing on listing speech-enabled models with a specific output scope.

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?

Provides a concrete use case: 'Use this to point OPENROUTER_VOICE_MODEL at a different provider without touching the code.' This tells the agent when to invoke it. It does not explicitly contrast with alternatives or mention when not to use it, but the clear purpose and sibling names make the context unambiguous.

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