auto_cleanup_vocal

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

With no annotations provided, the description carries full burden and discloses: async behavior ('Runs in background — returns a job_id immediately'), the complete processing pipeline with specific parameters (3:1 compression, 10dB noise reduction), and concurrent usage restrictions. Deducted one point because it doesn't explicitly state whether the operation is destructive/modifies files, though this is implied by 'cleanup' context.

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 visual hierarchy (ONE-CLICK VOCAL CLEANUP header, Pipeline section, Args section, IMPORTANT warning). Information is front-loaded with purpose, followed by implementation details, then constraints. No redundant or filler text; every sentence provides actionable information.

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 annotations and no output schema, the description adequately covers the async job pattern (job_id return), monitoring mechanism, and specific vocal-tuning differentiators. Could be improved by briefly describing error states or completion indicators, but sufficiently complete for safe invocation.

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 (parameter lacks description field), the description fully compensates by explaining 'remove_noise: Apply noise reduction using first 0.5s as noise profile' and adding critical operational constraints about the 0.5s silence requirement. This goes beyond basic type information to explain semantics and usage impact.

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?

Description explicitly states 'Professional processing for singing and studio vocals' and details the specific audio pipeline (DC offset > HPF 100Hz > noise reduction, etc.). It clearly distinguishes from sibling cleanup tools by specifying 'Tuned for singing — presence boost for clarity, higher HPF for plosive removal,' differentiating it from auto_cleanup_interview, auto_cleanup_podcast, and auto_cleanup_audio.

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 prerequisites ('If remove_noise is True, the first 0.5 seconds should be room tone / silence'), warns against concurrent usage ('DO NOT call this again if a pipeline is already running'), and directs to the correct monitoring tool ('Use check_pipeline_status to monitor'). It clearly establishes when to use this versus checking status.

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