transcription_set_model

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

No annotations are provided, so the description carries the full burden. It discloses experimental status, background execution, immediate job_id return, and model download sizes with accuracy tradeoffs. This is strong behavioral disclosure for a tool with no structured annotation coverage.

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 core purpose, immediately followed by the critical usage warning. The model-size list and Args section are compact and every sentence contributes actionable information without filler.

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 simple one-parameter background tool with no output schema, the description covers the purpose, the exact conditions to call it, the behavioral execution model, the parameter options, and what to expect in return ('returns a job_id immediately'). Nothing essential 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?

The input schema only documents model_size with a default and has 0% description coverage. The description fully compensates by enumerating every valid value ('tiny', 'base', 'small', 'medium', 'large-v3') and adding size and accuracy guidance for each, plus a recommendation.

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 immediately clarifies the tool's narrow role and distinguishes it from transcription tools like transcribe_audio by stating that transcription tools handle model loading automatically.

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 gives an explicit exclusion: 'Do NOT call this before transcribe_audio,' which tells the agent exactly when not to use it.

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