score_ai_text

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

No annotations exist, so the description carries the full behavioral burden and does so well: it discloses the dual execution path (real detector API vs local heuristic) and enumerates what the heuristic measures (burstiness, stock phrases, em dash density, lexical variety). It also flags an important interpretive caveat (no score proves authorship).

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?

Purpose and scale are front-loaded in the first sentence, followed by mechanism and usage notes. The 'Step 7.' prefix is workflow scaffolding rather than tool definition, and the mid-sentence line break adds slight noise, but overall it is tight and earns its sentences.

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?

With one required parameter, no output schema, and no annotations, the description covers everything an agent needs: what it returns (0-100 score), the mechanism and its fallback, the reporting caveat, and the recommended call sequence relative to humanize_text.

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 0% for the single 'text' parameter, but the parameter is trivially inferrable from the description's subject ('how human the text reads'). The description adds scale semantics but no input format details, so it partially compensates for the coverage gap.

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?

States a specific verb (score), resource (text), and output scale (0-100, higher = more human), and ties itself to the humanize_text workflow. It does not explicitly distinguish itself from near-siblings like find_ai_words, so it is clear but not fully sibling-differentiated.

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?

Explicit sequencing guidance: check is_real_detector before presenting the number, always surface the authorship caveat, and run before and after humanize_text to show improvement. This tells the agent both when to call it and how to report it, with no inference required.

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