auto_cleanup_audio

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

Describes background execution, immediate job_id return, and lists the processing pipeline steps. Also notes side effects of verify parameter (extra exports). Without annotations, this provides solid behavioral context, though it could mention if it requires audio selection.

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: summary line, usage context, pipeline details, parameter args, and an IMPORTANT note. Every sentence adds value; no 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?

Given no output schema, description covers return behavior (job_id), monitoring via check_pipeline_status, and pipeline steps. Complete for a background cleanup tool.

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 tool description provides thorough parameter documentation: explains remove_noise uses first 0.5s as noise profile, remove_clicks for vinyl/old recordings, and verify's trade-off. This compensates fully for missing schema descriptions.

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 that the tool safely removes noise and artifacts without altering loudness or dynamics, distinguishing it from related effects like normalize and compressor. It uses specific verbs and resource (audio cleanup) and contrasts with sibling tools.

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 specifies when to use (audio levels already good, just cleanup) and when not to (when a pipeline is already running). Provides alternative monitoring tool (check_pipeline_status) and warns about verify parameter impact.

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