mcp_opendaw_auto_gain

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

The description discloses the iterative process (render, measure, adjust, re-render) and convergence tolerance (±1 LUFS), but lacks details on side effects like whether a Maximizer plugin is required or added, or if the project master is altered. Since no annotations are provided, the description carries the full burden but is somewhat incomplete.

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 concise (about 120 words), well-structured with an intro, process explanation, and parameter list. Every sentence adds value, and the purpose is front-loaded.

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 covers what the tool does, how it works, parameter details, and return values. It lacks information on prerequisites or side effects, but given the presence of an output schema, the return value listing is sufficient. Minor gaps prevent a perfect score.

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 description individually explains each parameter with examples and defaults (e.g., target_lufs with common values, filename format, sample_rate default 48000, max_iterations default 3). Since schema_description_coverage is 0%, the description fully compensates and adds meaning beyond the schema's field names.

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: 'Auto-adjust output volume to hit a target LUFS.' It specifies the iterative loop and target loudness standards, effectively distinguishing it from sibling tools like measure_lufs or render_and_analyze.

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 implies usage for final loudness normalization but does not explicitly state when to use this tool versus alternatives. No exclusions or comparisons to sibling tools are provided.

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