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

Even with annotations marking it read-only, open-world, and idempotent, the description adds substantial behavioral detail: it fans out across multiple sources, soft-fails on the USPTO sunset, treats empty FDA results as expected rather than errors, reports sources_used/sources_failed, falls back from GDELT to GNews, and returns resolved:false for private companies. This goes far beyond the annotations and helps the agent interpret results correctly.

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 for a tool of this complexity nearly every clause carries useful information: input forms, source list, output fields, failure semantics, and edge cases. It is front-loaded with trigger examples and the core purpose before diving into details, though the single dense paragraph could be structured slightly more cleanly.

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 full burden of explaining return values, and it does so thoroughly: it lists the major output sections, explains what each one contains, clarifies when an empty section is expected, describes fallback behavior, and covers the private-company resolved:false case. An agent has enough context to call the tool and interpret its response 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?

Schema coverage is 100% for both parameters, so the schema already documents that `type` accepts company/ticker and `value` accepts ticker, CIK, or name. The description reinforces this with examples and notes that names resolve via SEC EDGAR, but it does not add meaningful new parameter semantics beyond what the schema already provides.

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 identifies a specific verb and resource: it builds a full cross-source profile of a US public company in one parallel call. It also includes concrete trigger phrases and enumerates the exact sources and output fields, making it clearly distinguishable from single-purpose lookup tools.

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 clear when-to-use guidance: prefer this over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view. It does not explicitly name sibling alternatives like resolve_entity or compare_entities, but it states the intended context strongly enough for an agent to select it appropriately.

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