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

Annotations already provide readOnly, idempotent, and non-destructive hints, and the description substantially adds context: the fan-out across SEC, XBRL, USPTO, USAspending, and more; the `sources_used`/`sources_failed` semantics; the "empty section is a real no-data, not a bug" caveat; the USPTO API sunset soft-fail; and the private-company return shape. This goes well beyond what the annotations express.

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 it is densely informative and front-loaded with example queries and the primary directive. Each source and caveat earns its place, though some redundancy in the input-form explanation could be trimmed. It is appropriately sized for a complex tool with many return sections.

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?

There is no output schema, so the description carries the burden of explaining return value structure. It enumerates every expected field, describes failure semantics for each source, clarifies empty results, and explains resolution behavior. Nothing essential is missing for an agent to call this tool correctly and interpret its response.

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 schema already covers both parameters at 100%, so the baseline is 3. The description adds meaningful nuance by stating that `type` accepts "company" or "ticker" interchangeably and that `value` may be a ticker, zero-padded CIK, or company name with name resolution. It reinforces and clarifies the schema rather than merely repeating it, earning a 4.

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 names a specific verb+resource: it builds a "full cross-source profile of a US public company in ONE parallel call." It lists concrete example queries and enumerates the exact data sources, making the tool's purpose unmistakable and distinct from sibling tools like resolve_entity 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?

It explicitly directs agents to "ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view," which is clear selection guidance. It also explains acceptable input forms and what to expect for private companies versus public ones, so agents know when the tool applies and what fallback behavior occurs.

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