transcription_set_model

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

Discloses experimental status, background execution (returns job_id immediately), need for separate setup, and model sizes with file sizes and accuracy trade-offs. No annotations exist, so description fully carries the burden.

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?

Well-structured with clear sections (experimental warning, usage guidance, background behavior, model size table). Every sentence is informative and not redundant.

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?

Completely describes purpose, usage constraints, parameter details, execution model, and monitoring. No gaps given the tool's simplicity and lack of output schema.

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?

Despite 0% schema description coverage, the description lists the single parameter model_size with detailed choices (tiny/base/small/medium/large-v3) and explains size and accuracy, adding significant meaning beyond the schema's default and string type.

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 tool pre-downloads a whisper model. It explicitly distinguishes from sibling transcription tools by warning not to call it before transcribe_audio, making the purpose specific and non-redundant.

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?

Provides explicit when-to-use ('ONLY call this if the user explicitly asks to download or change the model') and when-not-to ('Do NOT call this before transcribe_audio'), along with alternative (transcription tools handle loading automatically) and follow-up action (use check_transcription_status).

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