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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, and the description adds significant behavioral context beyond these: patents API sunset with soft-fail behavior, sources_used/sources_failed indicating real data availability, fda_products absence for small-molecule/generic-only companies explained as expected, and private company handling with explicit notes. No contradiction with annotations; the description enhances transparency significantly.

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?

While long, the description is densely packed with high-value information and each sentence earns its place. It front-loads purpose with examples, then systematically enumerates sources and return fields with clear separators (semicolons, parentheticals), and ends with parameter details. It is well-structured and not wasteful; the complexity of the tool justifies the length.

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?

Given the tool's high complexity (multiple sources, many return sections, no output schema), the description is remarkably complete. It details every return field (cik, company_name, recent_filings, fundamentals, patents, federal_contracts, fda_products, hiring, news, LEI, sources_used/failed), explains edge cases (resolved:false, empty sections, patent soft-fail), and provides parameter handling guidance. There is nothing an agent needs to know that 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% for both parameters, providing baseline 3, but the description enriches meaning considerably: it gives concrete examples ('AAPL', '0000320193', 'Moderna'), explains name resolution via SEC EDGAR, clarifies that type accepts both values interchangeably, and details edge cases (private companies returning resolved:false). This goes well beyond the schema's enum and value descriptions, fully compensating for any ambiguity.

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 states exactly what the tool does: 'full cross-source profile of a US public company in ONE parallel call.' It enumerates the specific data sources (SEC EDGAR, XBRL, USPTO, USAspending, FDA Purple Book, DOL LCA, GDELT/GNews, GLEIF) and return sections, making it unmistakable and distinct from sibling tools like deep_research or compare_entities. The explicit directive 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups' distinguishes it from alternative approaches.

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 provides explicit when-to-use guidance: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also clarifies usage edge cases: passing a ticker, CIK, or name; private companies returning resolved:false; patents soft-failing after sunset; and empty sections being 'real no-data, not a bug.' It implicitly signals when not to use it (for specific single-source queries) by contrasting with chained lookups.

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