lookup_entity

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

With no annotations provided, the description carries the full disclosure burden. It transparently covers the paid nature ($0.05, x402 USDC), payment handshake, registry sources, match behavior (exact vs candidate), and return fields (legal name, status, type, jurisdiction, identifiers, provenance URL). It could add edge-case behavior (e.g., not-found responses), but the core transactional and lookup behavior is well disclosed.

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 description is dense but every sentence adds essential value: payment, registries, query modes, output fields, use cases, and payment handshake. It is front-loaded with the most critical operational detail (paid) and avoids redundant phrasing.

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?

For a paid, multi-registry lookup tool with a single unannotated parameter and no output schema, this description is remarkably complete. It explains what the tool does, how to invoke it, what it returns, when to use it, and how payment works — covering all information an agent needs to select and call it correctly.

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?

The schema has one opaque 'query' parameter with 0% description coverage. The description fully compensates by specifying accepted values: NPI, LEI, FDIC cert, CIK, EIN for exact matches, or a name for candidate matches. This leaves no ambiguity about how to populate the parameter.

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 uses a specific verb ('Verify') and names the resource ('entity / counterparty by identifier or name'), then goes further by enumerating the authoritative registries (NPI, LEI, FDIC, CIK, EIN). This clearly differentiates it from sibling search and screening tools, which focus on broader queries rather than exact registry-backed verification.

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?

Provides explicit instructions on how to query ('Pass an identifier ... or a name') and lists concrete use cases such as KYC, KYB, and counterparty due diligence. It does not explicitly mention exclusions or alternatives to sibling tools like screen_entity, but the context is clear enough for appropriate tool selection.

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