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

The description significantly expands on the annotations by disclosing soft-fail behavior ('USPTO PatentsView API sunset May 2025 — soft-fails until reactivated'), expected empty results for fda_products when a company has only small-molecule drugs, and the semantics of sources_used/sources_failed ('an empty section is a real "no data", not a bug'). It also documents resolved_from/resolved_to and resolved:false for private companies, all consistent with the readOnly/idempotent hints.

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 every clause contributes meaning—covering sources, return fields, failure modes, and routing. It is front-loaded with query examples and the 'ALWAYS PREFER' directive. The semicolon-heavy structure is dense but scannable; a more segmented layout would improve readability, though nothing is redundant.

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?

Since there is no output schema, the description carries the burden of enumerating return fields (cik, recent_filings, fundamentals, patents, federal_contracts, fda_products, hiring, news, LEI, sources_used/failed). It also covers data-source quirks, failure modes, and edge cases, making it fully self-sufficient for an agent to select and invoke correctly.

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?

While schema coverage is 100%, the description adds valuable nuance: `type` values 'company' and 'ticker' are explicitly interchangeable and accept the same `value` shapes, and `value` can be a ticker, zero-padded CIK, or company name resolved via SEC EDGAR. This clarifies edge cases and reduces the chance of an agent passing the wrong type or format.

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 queries ('Tell me about X', 'research Acme') and immediately states the core function: 'full cross-source profile of a US public company in ONE parallel call.' This is a specific verb+resource+scope that clearly differentiates it from chained single-source 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 gives an explicit routing rule: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also specifies accepted inputs (ticker, CIK, or name), how names resolve, and what happens for private companies, giving the agent enough context to decide when this tool is appropriate.

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