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

With no annotations, the description carries full behavioral burden and meets it: it calls out statelessness, the free-trial limit, the refusal behavior with the x402 recipe, and that data_consent enables storage in a training corpus. It also discloses that wallet_address never changes scoring and that applying the suggestion yields a re-score. This is unusually transparent about side effects and state.

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 core purpose is front-loaded in a strong first sentence, but the description is a single long block with promotional/persuasive phrases like 'watch the number move' and 'Designed for permanent, always-on use.' These add color but not operational clarity. It is not egregiously verbose, but a more structured, less hype-heavy presentation would serve an agent better.

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 main workflow, the return values, the free-trial behavior, and the x402 continuation path—important context for a tool with no output schema and no annotations. It points to /llms.txt for the permanent setup recipe and explains how wallet_address changes the response. It does not detail error cases or the exact shape of sub-scores, but the core usage is fully specified.

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 all 10 parameters are already documented in the schema. The description mostly re-frames existing schema content (e.g., data_consent as 'the price of the free trial', wallet_address fee quoting) rather than adding genuinely new semantic meaning. It does not elaborate on the optional marketing params (source, contact, bot_role, model_family) beyond what the schema says.

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 clear verb and resource: 'Scores AI-generated text plus a response delay against a human personality profile.' It then specifies concrete outputs (EQ percentage, pass/fail, trait sub-scores, suggested_prompt_addition), leaving no ambiguity about what the tool does. It also differentiates from all sibling tools, which are about stats, pricing, profiles, fees, features, or widgets—none compete with text scoring.

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 explicitly frames the tool for 'permanent, always-on use' and explains the free-trial flow plus the x402 continuation after exhaustion. It doesn't name alternatives or when-not-to-use, but no sibling tool offers the same capability, so exclusion guidance isn't necessary. The reference to /llms.txt for setup and to wallet_address for fee quoting gives practical context.

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