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

The description discloses far more behavioral detail than annotations alone provide: it fans out across multiple data sources, explains what each returned section contains, and explicitly handles edge cases like empty sections being 'a real "no data", not a bug,' the USPTO API sunset soft-fail, FDA product absence being expected, and private companies returning resolved:false with notes. This is exactly the behavioral context an agent needs to interpret ambiguous results.

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 long, but it is front-loaded with the purpose and routing rule, then organized by source and return section. Nearly every sentence carries actionable information; however, there is some repetition (e.g., the interchangeable type semantics are stated both in prose and at the end), and the dense run-on style could be trimmed for faster scanning without losing value.

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?

Because there is no output schema, the description must carry the full burden of documenting return shape and edge cases. It does so thoroughly: it names every returned field (cik, recent_filings, fundamentals, patents, federal_contracts, fda_products, hiring, news, LEI, sources_used/failed), explains input permutations, and describes failure/success semantics. Nothing essential is missing for an agent to invoke and interpret the 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?

Input schema coverage is 100%, so the baseline is 3. The description adds meaningful semantics: it clarifies that 'type' accepts 'company' or 'ticker' interchangeably and that 'value' can be a ticker, zero-padded CIK, or company name, including how names resolve via SEC EDGAR. This supplemental guidance goes beyond the schema's enum and generic examples without being redundant.

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 example queries and a crisp statement of purpose: 'full cross-source profile of a US public company in ONE parallel call.' This names a specific verb (profile), a specific resource (US public company), and an explicit contrast to 'chaining single-pack SEC/XBRL/news lookups,' which distinguishes it from narrower sibling tools. The list of sources and returned sections further makes its scope 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?

The description contains an explicit routing rule: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' This gives a clear trigger (holistic company request), an alternative (chaining single-pack lookups), and a preference directive. It does not enumerate cases where this tool should NOT be used (e.g., single-fact lookups or entity resolution alone), so it falls just short of a perfect 5.

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