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

Annotations already promise a safe read-only, idempotent operation, and the description adds substantial behavioral detail: soft-failing USPTO API until reactivation, sources_used/sources_failed/sources_skipped tri-state semantics, empty sections meaning real no-data rather than bugs, and the SIC-based skip rule for the Purple Book. It also explains name resolution via EDGAR and private-company handling. This far exceeds what annotations convey.

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 not bloated: every source, return field, and state has a purpose. It front-loads trigger phrases and the core value proposition before diving into details. The single-paragraph format is a bit heavy, but given the tool's complexity it is structured and readable.

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?

There is no output schema, and the description compensates by enumerating the return sections, source states, skip reasons, and the resolved/resolved_from/resolved_to details. It also covers input resolution, failure semantics, and the meaning of empty arrays. Nothing critical is missing for an agent to call this tool 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 100% for both parameters, and the description reinforces and expands on them: type takes company or ticker interchangeably and value can be a ticker, zero-padded CIK, or company name. It provides a concrete example and describes the resolution outcome for private companies, making parameter selection unambiguous. This adds real value beyond the schema.

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 concrete user phrasings and states a clear mission: build a full cross-source profile of a US public company in one parallel call. It distinguishes itself from single-domain lookups by explicitly naming the fan-out across SEC, XBRL, patents, contracts, FDA, H-1B, news, and GLEIF. The resource and scope are unambiguous.

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?

It explicitly says to ALWAYS PREFER this tool over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view. It also covers edge cases like private companies returning resolved:false and person/place not yet supported, which helps an agent decide when it is not applicable. It could name more sibling tools like company_facts or compare_entities, but the primary routing rule is clear.

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