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

With no annotations provided, the description carries the full behavioral burden and does substantial work: it discloses the accountless operation mode, the normalization behavior, the exact return metadata (confidence, freshness, retrieval time, evidence hash), and per-call pricing via x402. It stops short of describing failure modes (e.g., no-match or low-confidence behavior), which is a gap for a paid external lookup, but the disclosure is meaningfully richer than the schema alone.

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?

A single dense sentence that front-loads purpose, then delivers scope, behavior, and pricing in order of importance. Every clause earns its place — accountless mode, normalization, return metadata, and cost — with zero filler or repetition of schema content.

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 lookup tool with no output schema, the description compensates well by disclosing the return envelope (confidence, freshness, retrieval time, evidence hash). It lacks guidance on zero-match/low-confidence handling and data-source caveats, which matter for a paid API, but the core call contract — inputs, outputs, cost — is sufficiently covered for an agent to invoke 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?

Schema coverage is 86%, so the schema already documents the parameters well. The description adds genuine framing value with 'normalize identity signals,' clarifying that the identity params (name, email, phone, company, location, linkedin_url) are fuzzy inputs to be normalized rather than exact-match keys, and 'requested enrichment fields' maps directly to the required requested_fields param. This framing goes beyond the schema's simple field descriptions.

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+resource ('people-enrichment lookup'), the scoping qualifier ('Accountless'), and the behavioral core ('normalize identity signals and return requested enrichment fields'). The addition of return metadata (confidence, freshness, retrieval time, evidence hash) makes it clearly distinguishable from siblings like people_record_normalize (normalization-only) and people_search (search vs. enrich).

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 'Accountless' qualifier and '$0.28/call via x402' pricing imply this is the pay-per-call, no-account path versus account-based alternatives, which provides useful context. However, it never explicitly names sibling tools or states when NOT to use it (e.g., when you have an account, or when you only need normalization, which people_record_normalize covers). Usage is implied rather than stated.

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