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

No annotations are supplied, so the description carries the full behavioral burden. It does useful work by disclosing the return shape (0-100 score, points/max_points, A-F grade, per-check breakdown) and that it mirrors enterno.io/ai-check, but it omits cost/latency, rate limits, auth requirements, and anything about failure modes for unreachable domains.

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?

Front-loaded with the action and purpose, then the signal list, then the return contract — a sensible order. The parenthetical 20-signal enumeration is long but earns its place by signaling coverage, and the external URL anchor is compact.

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 no output schema, the description correctly steps in to explain return values (score, points/max_points, grade, per-check breakdown), which is exactly what an agent needs. The remaining gap is sibling disambiguation against check_ai_check, which for a 40+ tool family is a meaningful omission.

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?

There is only one parameter and the schema already documents it fully ("Domain (with or without scheme)", 100% coverage). The description's phrase "a domain" adds no format, normalization, or validation detail beyond the schema, so the baseline 3 applies.

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 and resource ("Audit a domain for AI-agent discoverability") and enumerates the concrete signal categories covered (agents.json, llms.txt, MCP/A2A cards, robots.txt AI policy, etc.), so an agent knows exactly what the tool inspects. It loses the top score because it never distinguishes itself from the near-identically named sibling check_ai_check, leaving real ambiguity about which one to call.

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 use case is implied clearly enough — auditing a domain's AI-readiness — but there is no explicit when-to-use/when-not guidance, no prerequisites, and no routing to or away from check_ai_check or the related check_robots/check_schema siblings. The agent must infer the selection from names alone.

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