auto_cleanup_podcast

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

The description discloses that the tool runs in the background, returns a job_id, only reduces peaks if too hot (never boosts), and outlines the exact pipeline steps. It also warns about the noise profile requirement. Since there are no annotations, this description carries the full burden and handles it well.

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 well-structured with headings and bullet-like lines, starting with a clear purpose. It includes necessary pipeline details and parameter explanations without excessive fluff, though it slightly repeats default values.

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?

The description explains the return value (job_id), how to monitor progress, and the overall workflow including the optional LUFS normalization. It sets expectations for the pipeline and parameter behavior, making it complete for an agent to use the tool correctly.

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 has two boolean parameters with no descriptions, but the description explains each parameter's meaning (remove_noise applies noise reduction using first 0.5s as noise profile, remove_silence truncates long silences) and their defaults. This adds meaningful semantics beyond the schema.

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's purpose as one-click podcast cleanup with professional broadcast-quality processing, and it specifies it returns a job_id and can be monitored via check_pipeline_status. This gives a specific verb and resource, distinguishing it from general cleanup 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?

The description explicitly instructs to use check_pipeline_status to monitor, warns not to call again if a pipeline is already running, and suggests using loudness_normalize if needed. It also notes it is safe for badly recorded audio, providing clear usage context.

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