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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false, but the description goes far beyond. It discloses the multi-source fan-out (SEC EDGAR, XBRL, USPTO, news, GLEIF), the exact return fields, and important caveats: patents API sunset and soft-fail, GDELT→GNews fallback, and the format for recent_filings URIs. This is rich behavioral context that annotations cannot convey.

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 information-dense. It front-loads with example queries and the core value proposition, then systematically lists sources and return fields. Every clause adds value, but the density could be slightly trimmed without losing meaning. Given the tool's complexity, the length is justified, but it is not as lean as the TDQS exemplar.

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 fully specify the return structure, and it does: cik, company_name, recent_filings (with up to 5 and URI format), fundamentals (specific fields and sorting), patents (with sunset caveat), news (with fallback), and LEI. It also covers input restrictions and alternatives. For a tool of this complexity, the description is complete.

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 input schema already provides 100% coverage with descriptions for both parameters. The description reinforces this by giving concrete examples ('AAPL' or zero-padded CIK '0000320193') and re-emphasizes the 'names not supported' constraint, adding clarity beyond the schema's generic description. It earns a 4 because it adds examples and practical context 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 ('Tell me about X', 'research Acme', 'brief me on Tesla') that make the intended use unmistakable. It then states the core purpose: 'full cross-source profile of a US public company in ONE parallel call' and explicitly differentiates from chaining single-purpose lookups, positioning it as the preferred holistic tool. It also distinguishes itself from sibling tools like resolve_entity by noting the input limitation and alternative.

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 gives 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 provides a clear when-not-to-use with an alternative: 'names not supported (use resolve_entity first if you only have a name).' This is a textbook example of directing the agent to the right tool.

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