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

Annotations already declare readOnly/openWorld/idempotent/non-destructive, so the description carries a lower burden, but it still adds substantial behavioral context: the fan-out across SEC, XBRL, USPTO, USAspending, FDA, DOL, news, and GLEIF; the soft-fail for the USPTO API sunset; the expectation that an FDA section may legitimately be empty; and the meaning of sources_used/sources_failed. This goes well beyond the structured metadata.

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 front-loaded with examples and the 'ALWAYS PREFER' directive, then systematically enumerates the data sources and return sections. There is minor redundancy around input forms at the end, but the length is justified by the tool's complexity and the absence of an output schema.

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 complexity, no output schema, and two open-ended parameters, the description covers all necessary call decisions: input forms, accepted type values, returned fields with caveats (patent sunset, FDA empty-section expectation, H-1B metrics), behavior for private companies, and how to interpret empty results. An agent could invoke this tool correctly with only the description.

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%, and the description adds value beyond the schema by clarifying that 'value' accepts a ticker, zero-padded CIK, or company name interchangeably, that type='company' and type='ticker' behave identically, that names resolve via SEC EDGAR's company-name match, and how unresolved private-company cases are reported. This is more than the schema alone provides.

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 natural-language examples ('Tell me about X', 'research Acme', 'brief me on Tesla') and states the resource: a full cross-source profile of a US public company in one parallel call. It clearly distinguishes itself from single-pack lookups and sibling tools by the directive 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups'.

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 says when to prefer this tool, names the alternative pattern it replaces ('chaining single-pack lookups'), and specifies accepted input forms — ticker, CIK, or company name. It also preempts common confusion: private companies yield resolved:false with an explicit notes line, and 'company'/'ticker' types are interchangeable.

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