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

Annotations only declare the generic profile (not read-only, not idempotent, not destructive, open-world). The description adds materially new behavior: the call spends real money from the account balance, billing is per second/minute or per token depending on model, and cost surfaces in usage.cost_rub. That monetization disclosure is exactly the kind of context an agent needs before invoking.

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?

Three tight paragraphs: identity/catalogue, input constraint plus size guidance, then cost. Front-loaded and every sentence carries load; the model-name list is short and useful for orienting the agent.

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 paid, non-idempotent, open-world transcription call with full schema coverage and no output schema, the description covers all the decision-relevant gaps: model discovery, input form, size ceiling with a fallback route, and billing. Nothing needed to call it correctly is missing.

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%, so all four parameters are already documented, making 3 the baseline. The description reinforces audio_base64 ('pass the file content itself, links are not accepted') but adds little syntax or format detail the schema does not already carry.

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 and resource ('Transcribe a recording to text') plus the model catalogue it draws from, and pins the operation to a known HTTP equivalent (POST /v1/audio/transcriptions with base64 audio). This is enough to separate it from sibling tools like speech_create and youtube_transcribe without opening either 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?

Explicitly routes the agent: use this for clips of seconds, send longer recordings through the HTTP API instead, and find the model id via models_search. It also states a hard precondition (base64 content required, links rejected), leaving nothing to inference.

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