auto_cleanup_interview

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

The description discloses that it runs asynchronously (returns job_id immediately) and specifies the exact processing stages (DC offset, noise reduction, compression, LUFS loudness). It also explains the optional noise reduction parameter's behavioral requirement (first 0.5s must be room tone/silence). Since no annotations are provided, the description carries the full burden, and while it covers the key behavioral aspects, it doesn't address reversibility, permissions, or failure modes.

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 clearly formatted with a summary line, pipeline, and args section, making it scannable. However, it's somewhat verbose with the ALL-CAPS headline and trailing sentence about natural dynamics, which could be tightened. The structure is good but not maximally efficient.

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 single-parameter background tool with an explicit pipeline description and monitoring pointer, the description covers the essential operational context. It lacks details about failure behavior, what happens after processing completes, and how to interpret the job_id, but given the simplicity and the check_pipeline_status reference, it's reasonably 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 coverage is 0%, so the description must carry the parameter meaning, and it does. The remove_noise parameter is explained in plain language ('Apply noise reduction using the first 0.5s as a noise profile'), including its default value and a critical usage warning about what the first 0.5s of audio must contain. This goes beyond the bare schema and adds actionable guidance.

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 this is a one-click interview cleanup tool that applies light-touch processing for dialogue and multiple speakers, with an explicit pipeline listing. It distinguishes itself from sibling tools by name ('Lighter than podcast') and by function (interview-specific processing), though it doesn't name a specific sibling tool directly.

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?

The description states it runs in background and points to check_pipeline_status for monitoring, which gives clear usage context. It notes this is 'Lighter than podcast' and preserves natural conversation dynamics, which implies when to prefer this over podcast or other cleanup tools, but doesn't explicitly state when NOT to use it or name an alternative.

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