auto_cleanup_interview

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

With no annotations, the description carries full burden. It discloses async behavior (returns job_id), pipeline steps, consequences of verify (costs extra exports), and the requirement for noise profile if remove_noise is True. Lacks details on error handling or idempotency but covers key behavioral aspects.

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 bullet points and a clear pipeline breakdown. Front-loaded with purpose. While not extremely concise, every sentence adds value without redundancy.

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 3 boolean params, no output schema, and async behavior, the description explains the pipeline, parameter effects, async nature, and concurrency constraint. It does not detail the job_id format or how check_pipeline_status works, but those are within sibling tools.

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 coverage is 0%, but the description explains all three parameters: remove_noise (uses first 0.5s as noise profile), remove_silence (truncates silences), verify (costs extra exports). Adds meaning beyond raw schema types and defaults.

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 'Light-touch processing for dialogue and multiple speakers' and differentiates from siblings like auto_cleanup_podcast by noting it's 'Lighter than podcast' and preserves natural conversation dynamics.

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 explicitly says when to use (for interviews, lighter than podcast), provides a warning not to call again if pipeline is running, and directs to check_pipeline_status for monitoring.

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