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

Beyond the annotations (readOnly, idempotent, non-destructive), the description discloses significant behavior: parallel fan-out across multiple sources, soft-fail for the USPTO API sunset, FDA products returning empty for small-molecule-only companies, empty sections meaning real "no data," and name resolution behavior. It also explains the response structure at a high level.

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 sentence carries useful information. It front-loads usage examples, then systematically covers source fan-out, return highlights, failure semantics, input types, and edge cases. There is no filler or redundancy; the density is justified by the tool's complexity.

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?

For a complex multi-source tool with no output schema, the description is remarkably complete. It covers the full list of sources, expected output fields, failure modes, input formats, and the private-company edge case. An agent has enough information to invoke the tool correctly and interpret its results without additional documentation.

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%, so the baseline is 3, but the description adds meaningful value beyond the schema: it clarifies that `type` accepts "company" or "ticker" interchangeably, gives concrete examples for ticker, CIK, and company name, and explains edge-case behavior like zero-padded CIK and private-company resolution. This goes beyond simply restating parameter descriptions.

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 phrasings and immediately defines the tool as a "full cross-source profile of a US public company in ONE parallel call." It clearly distinguishes this from single-source lookups and sibling tools like resolve_entity or compare_entities by emphasizing holistic multi-source aggregation.

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 to "ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view." It also gives concrete guidance on accepted inputs and expected behavior for private companies, which helps an agent decide when this tool applies versus when it will not resolve.

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