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

Annotations already mark it read-only, idempotent, open-world, and non-destructive; the description goes well beyond those by detailing parallel fan-out, source fallbacks (GDELT→GNews, USPTO soft-fail), the meaning of empty sections, and private-company resolved:false behavior. This is rich behavioral context that helps an agent set expectations and interpret results.

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 text is front-loaded with purpose and preference, and the long list of return sections is justified by the absence of an output schema. But it is a single dense paragraph with repetitive query examples and no visual breaks, so it is not maximal in conciseness.

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 enumerates every major return group, gives expected-empty semantics, names the USPTO sunset, and covers failure/fallback behavior. Nothing an agent needs to decide or invoke correctly appears missing.

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 both parameters are already documented in the schema with the same examples and EDGAR-resolution note. The description restates that type values are interchangeable but adds no new format or constraint information beyond the schema, so the baseline 3 applies.

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 begins with concrete user queries, then states the action: 'full cross-source profile of a US public company in ONE parallel call.' This clearly differentiates it from siblings like resolve_entity or compare_entities by naming its scope and one-call nature. The list of sources and return sections makes the resource unmistakable.

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 gives an explicit directive: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' This establishes the primary use case with a strong when-to-use rule. It does not, however, name specific sibling tools to use instead for narrow lookups or comparisons, so exclusions are only implicit.

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