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

Annotations already mark the tool as readOnly, idempotent, and non-destructive, and the description complements this by revealing exact fan-out behavior: SEC EDGAR, XBRL, USPTO, USAspending, Purple Book, DOL LCA, news, and GLEIF. It also discloses important edge-case behavior: empty sections are 'a real no data, not a bug,' and private companies yield resolved:false with an explicit notes line rather than a bare failure. No contradictions with annotations.

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 and mostly front-loaded: the first sentence states the core purpose and the preferred usage context. The rest is a long list of return fields and edge cases that, while useful, is presented as one large run-on block and could be easier to scan with bullet points. Every sentence does contribute, so 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?

Even without an output schema, the description enumerates the return payload in detail: cik, company_name, resolved_from/to, recent_filings with URIs, fundamentals fields, patent/contract/FDA/H-1B/news/LEI sections, and sources_used/sources_failed. It covers input formats, name resolution, private-company behavior, and empty-section semantics, so an agent is fully equipped to invoke the tool and 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 coverage is 100%, so the baseline is 3, but the description goes beyond the schema by showing accepted value shapes ('AAPL', '0000320193', 'Moderna') and clarifying that type is interchangeable between 'company' and 'ticker'. It also explains resolution behavior for names and the private-company outcome, which the schema does not cover.

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 clear statement: 'full cross-source profile of a US public company in ONE parallel call.' It names the resource (US public company), the scope (cross-source), and the delivery mode (parallel call), and then enumerates the specific data domains returned. This distinguishes it from narrow single-source lookups and from sibling tools like resolve_entity, even without naming them explicitly.

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 description explicitly says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' giving a clear condition for selection. It also notes that private companies return resolved:false with notes, which warns against using it for non-public entities. However, it does not name sibling alternatives directly or state when-not-to-use scenarios beyond the holistic-view condition.

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