Normalize loudness

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

With no annotations, the description carries the full burden. It discloses the normalization standard, the intended result, and that video is untouched. However, it does not explain what happens when output_path is omitted (e.g., overwriting input) or any other side effects, which is important for a mutation tool.

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 two concise sentences, front-loaded with the main purpose and key outcome. Every clause adds value, and there is no redundancy or fluff.

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 there is no output schema and no annotations, the description should cover operational edge cases. It explains the primary use and result, but omits behavior when output_path is not supplied, potential overwriting, or return/error information, leaving some gaps for a tool with optional parameters.

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 description coverage is 100%, so the baseline is 3. The description does not add parameter-level detail beyond the schema; it simply explains the purpose without elaborating on input_path, output_path, or target_lufs semantics.

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 normalizes audio to broadcast-standard loudness (EBU R128), which is a specific verb+resource+standard. It also distinguishes itself from sibling tools like adjust_volume by targeting loudness normalization and explicitly noting video is untouched.

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 provides clear context on when to use it: 'fixes too-quiet or wildly varying audio' and 'matches typical podcast/social-media levels'. It does not explicitly name alternatives or exclusions, but the use case is well implied.

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