transcription_set_model

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

There are no annotations, so the description carries the full burden. It discloses background execution, immediate job_id return, the need for separate setup, and the experimental status. It does not discuss failure modes, idempotency, or disk-space effects, but the provided behavioral traits are substantive and useful.

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 front-loaded with the most critical usage guardrail, then setup and background behavior, then the model-size list. Every section earns its place, and the list format makes the tradeoffs scannable. It is detailed without being bloated.

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?

Even though there is no output schema and no annotations, the description covers the core operational facts: what the tool does, when to call it, that it returns a job_id immediately, how to monitor it, what setup is needed, and what parameter values mean. For a single-optional-parameter background tool, this is sufficiently complete.

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 0%, so the description must compensate. It lists the allowed model_size values with size, speed, and accuracy tradeoffs, which adds real meaning beyond the input schema. It doesn't explicitly note that omitting the parameter uses 'base' (though the schema default covers that) or how invalid values are handled, so it is strong but not exhaustive.

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 opens with a specific verb and resource: 'Pre-download a whisper model.' It also distinguishes itself from transcribe_audio by explicitly stating that transcription tools handle model loading automatically, so an agent can tell this tool apart from siblings without opening schemas.

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?

It gives an explicit call condition: 'ONLY call this if the user explicitly asks to download or change the model.' It also says when not to use it ('Do NOT call this before transcribe_audio') and names the alternative monitoring tool, check_transcription_status. This is direct and actionable guidance.

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