who: paid legal-entity lookup

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

Annotations cover only read/write safety, but the description discloses the dominant behavioral trait they miss entirely: each call spends $0.05 USDC on-chain from a PRIVATE_KEY wallet, with no subscription/credit model. It also lists the free-refusal outcome codes (invalid_subject, not_found, ambiguous, source_unavailable), the spend caps, and a known data gap for non-Latin scripts — well beyond what annotations provide.

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 single most decision-critical fact — that this spends real money — is front-loaded in the first sentence, followed by the return contract, then free-refusal cases, caps, and the known gap. Information is chunked and every sentence carries distinct, actionable content with no 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?

With no output schema, the description compensates by enumerating the exact return fields and all refusal result types. Combined with the cost model, spend caps, and coverage limitation, an agent has everything needed to decide whether to call it and how to interpret the result.

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% and the single q parameter is fully documented in the schema, including the ';jurisdiction' suffix and subdomain caveat. The description reinforces the ambiguity/jurisdiction handling but adds no syntax or format 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?

The description names the specific action (GLEIF Level 1 legal-entity lookup, exactly one record) and enumerates the returned fields (LEI, legal name, jurisdiction, entity/registration status, match quality, website), plus explicitly delimits scope with 'No addresses, no officers, no ownership graph, no guesses.' It is clearly distinguishable from the sibling who_terms, which is referenced as the pricing lookup.

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?

Gives explicit routing ('Check who_terms first if the price matters'), explicit correction guidance for ambiguity ('re-ask with an exact legal_name and a ;jurisdiction suffix'), and states the conditions under which the tool proactively refuses (price exceeds MAX_USD_PER_CALL or MAX_USD_PER_SESSION). This is when-to-use, when-it-won't-work, and alternative-tool guidance all in one.

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