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

Even with annotations readOnlyHint, openWorldHint, and idempotentHint true, the description adds substantial behavior beyond annotations: the fan-out across specific sources, soft-fail for USPTO sunset, GDELT→GNews fallback, empty-section semantics ('an empty section is a real "no data"'), and resolved_from/resolved_to behavior. It also clarifies edge cases like companies with only small molecules 'will show none here, that is expected, not a failure.' No contradiction 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 information-dense and well-organized conceptually, but it is a long single paragraph with repetitive query examples ('Tell me about X', 'research Acme', 'brief me on Tesla', 'what does Apple do', 'company profile for Microsoft', 'give me the rundown on NVDA', 'everything you know about $TICKER'). It could be tightened with fewer examples and bullet points for return fields. The front-loaded core is excellent, but the overall length hurts conciseness.

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 must carry the burden of explaining return values, and it does thoroughly: it enumerates cik/company_name, recent_filings with URIs, fundamentals sorts, patents, federal_contracts, fda_products, hiring, news, LEI, and sources_used/sources_failed. It also covers input variants, private-company behavior, and expected-empty cases. Nothing critical for invoking the tool is missing.

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 baseline is 3. The description adds concrete value by giving real-world examples ('AAPL', '0000320193', 'Moderna'), emphasizing zero-padded CIK, and explaining that type 'company' and 'ticker' are interchangeable and that names resolve via SEC EDGAR company-name match. This goes beyond the schema's terse wording, though it still partially restates what the schema already says.

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 strong declarative: 'full cross-source profile of a US public company in ONE parallel call.' It includes illustrative queries ('research Acme', 'brief me on Tesla') and names the resource (US public company) and the specific action (profile). It clearly distinguishes itself from siblings like resolve_entity by emphasizing the comprehensive cross-source aggregation.

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 states when to use it: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also provides a when-not: private companies return resolved:false, and 'person/place coming soon' implies the tool is currently limited to companies. This is clear routing guidance with named alternatives and conditions.

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