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

Annotations cover the safety profile (readOnly, idempotent, openWorld, non-destructive), and the description adds rich behavioral context beyond them: the soft-fail for USPTO patents, the GDELT→GNews fallback, the expectation-setting that fda_products will legitimately be empty for small-molecule companies ('that is expected, not a failure'), and the 'empty section is a real no data, not a bug' semantics for sources_used/sources_failed. It also discloses the resolved:false + notes behavior for private companies instead of a bare failure. This is exemplary edge-case disclosure.

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 trigger phrasings and the core purpose, and nearly every sentence carries information. However, the return-value enumeration is one massive run-on sentence that is hard to scan, and the eight example phrasings at the start are somewhat redundant. It is comprehensive but structurally dense; a list or shorter clauses would improve parseability without losing content.

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?

For a complex multi-source fan-out tool with no output schema, the description carries the full burden of explaining return values, and it does so thoroughly: the source list, the return fields (cik, company_name, recent_filings with URIs, fundamentals, patents, contracts, fda_products, hiring, news, LEI), the sources_used/sources_failed semantics, and both edge cases (private companies, expected-empty sections). Nothing an agent needs to correctly set expectations 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 the baseline is 3; both parameters already have descriptive schema text. The description adds value beyond that: concrete accepted values ('AAPL', '0000320193', 'Moderna'), the explicit statement that type values are interchangeable, name-resolution behavior via SEC EDGAR, and the resolved_from/resolved_to output behavior when a name is passed. The examples and resolution semantics genuinely aid parameter selection.

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 ('Tell me about X', 'brief me on Tesla', 'everything you know about $TICKER') followed by a precise statement of function: 'full cross-source profile of a US public company in ONE parallel call'. It specifies the resource (US public company), the action (cross-source profile), and the scope (SEC, XBRL, patents, contracts, FDA, H-1B, news, GLEIF), making it unmistakable what this tool does and how it differs from a narrow single-source lookup.

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?

Gives explicit routing guidance: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' This states the condition (holistic view) and what to prefer it over. However, it does not name specific sibling tools (e.g., resolve_entity for a bare CIK lookup, compare_entities for comparisons), so an agent must infer the full boundary against the 30+ siblings rather than being told.

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