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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the description's edge-case disclosure is additive rather than redundant. It flags the USPTO PatentsView API sunset and soft-fail behavior, warns that a company with only small-molecule/generic drugs will show an empty fda_products section as expected, and explains that empty sections are real 'no data,' not bugs. It also discloses private-company resolved:false behavior and name resolution via SEC EDGAR.

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 front-loaded with intent and examples, but it is also one dense, run-on paragraph mixing sources, return fields, edge cases, and input semantics. Every clause adds some value, but the lack of structural organization makes it harder to scan than it should be for such a behavior-rich tool.

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 carries the full burden of explaining return values and largely succeeds, listing keys like cik, company_name, resolved_from/to, recent_filings, fundamentals, patents, federal_contracts, fda_products, hiring, sources_used, and sources_failed, plus details like pipeworx URIs and period sorting. It also covers expected absences, soft failures, and private-company behavior, making it complete enough for an agent to invoke 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 already covers 100% of the parameters with descriptions and examples, so the baseline is 3. The description adds meaning by clarifying that `type` accepts 'company' or 'ticker' interchangeably, that `value` can be a ticker, zero-padded CIK, or company name, and that a private company resolves to false with a notes line—useful supplementary detail 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?

States that it returns a 'full cross-source profile of a US public company in ONE parallel call,' enumerating specific resources like SEC EDGAR, XBRL, USPTO, USAspending, FDA Purple Book, DOL LCA, and GLEIF. It also explicitly distinguishes itself from 'chaining single-pack SEC/XBRL/news lookups' with 'ALWAYS PREFER,' so an agent can tell it apart from related tools.

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?

Explicitly says to prefer this tool over chaining single-pack lookups when the user asks for a holistic view, and provides many natural-language invocation examples. It also defines accepted input shapes and private-company behavior, though it does not name sibling tools like deep_research or compare_entities or state when those should be used instead.

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