synthesize_speech

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the payment requirement, which is useful context, but lacks details on other behavioral traits such as rate limits, authentication needs beyond payment, what happens on failure (e.g., if payment ID is invalid), or the output format (e.g., audio file, URL). For a tool with no annotation coverage, this is a significant gap.

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 concise and front-loaded, consisting of two short sentences that state the purpose and a key requirement. There's no unnecessary information, and it efficiently communicates the core functionality. However, it could be slightly improved by integrating the payment requirement more seamlessly into the purpose statement.

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 complexity of a text-to-speech tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., an audio file, job ID, or error messages), nor does it cover behavioral aspects like performance or limitations. For a tool with 5 parameters and mutation-like behavior (implied by 'convert'), more context is needed to ensure proper 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?

The input schema has 100% description coverage, so the schema already documents all parameters (paymentId, text, modelId, voice, speed) with clear descriptions. The description adds no additional meaning beyond the schema, such as explaining parameter interactions or providing examples. With high schema coverage, the baseline score of 3 is appropriate.

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: 'Convert text to speech using AI.' It specifies the verb ('convert') and resource ('text to speech'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'generate_music' or 'generate_text', which are also AI generation tools but for different media types.

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 provides some usage context by stating 'Requires a valid paid payment ID,' which implies a prerequisite for using this tool. However, it doesn't offer explicit guidance on when to use this tool versus alternatives like 'generate_music' or 'generate_text', nor does it mention any exclusions or specific scenarios where this tool is preferred.

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