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

The description goes well beyond the readOnly/openWorld/idempotent annotations: it discloses the fan-out across SEC EDGAR, XBRL, USPTO, USAspending, FDA Purple Book, DOL LCA, GDELT/GNews, and GLEIF; explains soft-failure for the USPTO sunset; states that an empty fda_products section is expected for non-biologic companies; and clarifies that sources_used/sources_failed distinguish real no-data from bugs. This is rich behavioral context without contradicting 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 definition is long but well-structured: purpose and usage rule come first, then source fan-out, then return-field semantics, then input forms. Some information, like type interchangeability, is redundant with the schema, but the length is largely justified by the tool's complexity and by 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?

With no output schema, the description carries the burden of explaining return values, and it does so exhaustively: cik, resolved_from/resolved_to, recent_filings URIs, fundamentals sorting, patents soft-fail, federal contracts, FDA products with expected empties, H-1B hiring, news fallback, LEI, and sources_used/sources_failed. It also specifies edge-case behavior for private companies, making the tool fully callable.

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, so the baseline is 3. The description adds meaning by clarifying that 'type' accepts 'company' or 'ticker' interchangeably, that 'value' can be a ticker, zero-padded CIK, or company name, and that names resolve via SEC EDGAR matching. This helps an agent construct valid calls beyond what the schema states.

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 trigger phrases and a crisp definition: 'full cross-source profile of a US public company in ONE parallel call.' It identifies the resource (US public company), the action (profile/research/brief), and differentiates itself from single-pack SEC/XBRL/news lookups, which helps an agent distinguish it from sibling tools like compare_entities or deep_research.

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?

Explicit when-to-use guidance is front-loaded: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also names the alternative ('single-pack lookups') and describes expected behavior for non-matches, such as private companies returning resolved:false. This gives clear decision-making context.

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