speech_to_text

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

With no annotations provided, the description carries the full burden for behavioral disclosure. It mentions the model and supported formats but omits return structure, error behavior, or any processing side effects, leaving significant gaps for the agent.

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 a single sentence that leads with the core action, includes the model name, and adds only the relevant mention of supported formats. No redundant or filler content, so it is highly concise and well-structured.

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?

The tool is simple with fully documented parameters, but the absence of an output schema means the description could clarify what the transcription returns. It does not address error cases or language auto-detection behavior, leaving some contextual gaps despite its clarity.

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?

All three parameters have detailed descriptions in the schema (100% coverage), so the baseline score applies. The tool description adds no additional parameter-specific meaning beyond the schema, making the schema the primary source of semantic information.

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 'Transcribe' and the resource 'audio file to text', naming the specific model (scribe_v1). It distinguishes from sibling tools like text_to_speech by indicating directionality, and adds format support (WAV, MP3) for concrete 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?

The description clearly implies when to use this tool: when you have an audio file and need text transcription. It provides clear context but does not explicitly mention alternatives or exclusions, so it stops short of a 5.

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