voice_score

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

With no annotations, the description carries the full burden. It richly discloses how scoring works: curated slop lexicon, structural signals, comparison against the user's baseline, and thresholds (>85 human, <50 slop). It also notes that voice rules are returned, giving the agent a complete behavioral model.

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 efficient and front-loaded: purpose in the first sentence, usage directive second, then methodology and thresholds. Each sentence earns its place with no 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 the tool's complexity and lack of output schema, the description covers what it does, when to use it, how it evaluates, thresholds, and that it returns voice rules. This is complete enough for an agent to invoke it 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?

Both parameters (path and text) are fully described in the schema, so coverage is 100%. The description adds no additional parameter meaning beyond what the schema already provides, matching the baseline of 3.

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 defines the tool as scoring a draft 0-100 for AI tells and returning line-level findings. This is a specific verb+resource+output that distinguishes it from sibling voice_* 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?

It explicitly states when to use the tool: 'CALL THIS ON EVERY DRAFT before showing it to the user.' This gives clear usage context, though it does not mention when not to use it or alternative tools.

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