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

With no annotations, the description carries the full disclosure burden and does so well: it states the 10 MB limit, that you are charged only if the transcript is delivered, that no API key/account is needed, and the $0.02 x402 (USDC) payment model. Only minor gaps remain (no explicit rate limits or error behavior).

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?

Front-loads the core action and input modes, then layers return shape, language coverage, and pricing. Dense but every clause adds value; only the pricing sentence could arguably be trimmed.

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 4-param tool with no output schema and no annotations, the description covers inputs, formats, size limit, return payload (text, language+probability, duration, segments), optional word timestamps, language breadth, and cost. An agent has everything needed to call it correctly.

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 coverage is 100%, so the baseline is 3, but the description adds meaning beyond the schema: base64 audio is POST-only, words=true yields word-level timestamps, and language is an optional ISO code to skip detection. This meaningfully augments the field docs.

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?

States a specific verb (transcribes) and resource (audio file) and pins the exact engine (Whisper large v3 turbo). This clearly distinguishes it from siblings like ai_speech and ai_translate without needing to open any schema.

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?

Explains the two invocation modes (https URL or base64 via POST) and the words=true option, giving practical context for calling it. It stops short of naming when to choose this over ai_speech or ai_translate, so it is clear context without explicit alternatives.

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