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

With readOnlyHint/idempotentHint already covering the safety profile, the description still adds real behavior: a hard cap of 10 results, the relevance ordering ('words matched, then rules specific to the features, then binding law in force'), and the informational-not-legal-advice limit. The only soft spot is that the stated 'at most 10' is not reconciled with the separate `limit` parameter, which could accept other values.

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 a one-sentence purpose, then labeled blocks for when-to-use, per-parameter meaning, ranking, and limits — an agent can skim to what it needs. It is dense and slightly long, with a bit of overlap between the when-to-use examples and the feature list, but no sentence is filler.

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?

No output schema exists, yet the description specifies returned fields (lane, citation, official link, applies-from date, TwinEthos explorer page), the result cap, and the ranking order. Combined with the safety annotations, an agent has everything needed to call it and interpret the response.

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 0%, so the description carries the load and mostly does: jurisdictions are 'market codes (EU, US-CA, US-CO; US matches every state)', features are enumerated patterns (chat, rag, agentic, decision_pipeline, content_generation, classification, recommendation, embeddings), and text is defined as words matched in titles, summaries and tags with examples. The fourth parameter, `limit`, is never explained — and the 'at most 10' phrasing arguably conflicts with it.

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: 'Find the AI-law rules (EU AI Act, US state AI laws, China, Korea...) that apply to an AI feature in a market,' and it names the return unit (cited provision + official link). It also distinguishes itself from date-oriented siblings by stating 'It does not sort by date: use coming_into_force or recently_in_force for dates,' so an agent can route correctly without opening any schema.

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?

An explicit 'When to use' clause supplies concrete triggers ('which laws, regulations or standards apply to what they are building') plus example topics (chatbot disclosure, hiring, credit, deepfakes, minors, content labels, training data). The exclusions/alternatives are equally explicit, pointing date-based questions to coming_into_force and recently_in_force.

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