effect_repair

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

No annotations provided, so description carries full burden. Adds valuable error behavior ('Audacity will show an error popup if the selection is too long') and constraint details. However, omits key behavioral traits like whether the operation is destructive/permanent or what repair algorithm is used.

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?

Three sentences with zero waste: purpose (sentence 1), prerequisite (sentence 2), constraint/error (sentence 3). Front-loaded with specific scope limit. Every word earns its place.

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 zero-parameter effect with no output schema, description adequately covers critical constraints (128 sample limit) and prerequisites. Could be improved by explicitly stating the destructive nature of the repair operation, but the critical usage constraints are 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?

Zero parameters present, which per guidelines sets baseline to 4. Description correctly focuses on implicit prerequisite (audio selection) rather than inventing parameter documentation.

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?

Specific verb 'Repair' with clear resource 'damaged section of audio' and precise scope constraint 'max 128 samples'. The length restriction effectively distinguishes this from sibling repair tools like click_removal or noise_reduction.

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 states prerequisites ('Select the damaged region first') and constraints ('must be extremely short', 'max 128 samples'). Defines when to use (very short damage only). Lacks explicit naming of alternatives for longer sections, though the constraint implies when not to use it.

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