score_humanity

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

With no annotations provided, the description carries full responsibility for disclosure. It explains key behaviors: aggregate probability as arithmetic mean, failure handling (if every detector fails, aggregate is -1.0 and verdict is 'unknown'), and the default detector list. This goes beyond the schema and gives the agent a solid understanding of expected outcomes and edge cases.

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 well-structured: a clear one-sentence purpose, followed by concise details on defaults, adapter examples, and aggregate behavior. Every sentence earns its place without unnecessary fluff. It is appropriately sized for the tool's complexity.

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 the tool's purpose, parameters, default behavior, and edge-case output. Since an output schema exists, the return structure is already defined, but the description adds crucial context about the aggregate value and verdict string. For a tool with 2 parameters and moderate complexity, this is 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?

Schema coverage is 100%, but the description adds meaningful context. It clarifies the 'detectors' parameter with the default value, the empty-list behavior, and examples of allowed adapter names. The description also explains how outputs are aggregated, which is directly relevant to interpreting the parameters. This significantly enriches the schema.

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 opens with a specific verb and resource: 'Score how AI-like the text reads to one or more open detectors.' This clearly states the tool's function and distinguishes it from siblings like humanize (which transforms text) and detect_tells (which likely identifies tells). The reference to detectors also sets it apart.

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 scoring AI-likeness but does not explicitly state when to prefer this over alternatives. It mentions the default detector list and adapters, giving context about configuration, but it does not contrast with sibling tools or provide exclusion criteria. Usage guidance is clear enough for a scoring tool but not explicitly framed as 'use when...'.

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