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

Annotations already declare readOnly/openWorld/idempotent, but the description goes well beyond them: it discloses fan-out behavior across SEC, XBRL, USPTO, USAspending, FDA, DOL, news, and GLEIF; names the USPTO PatentsView sunset and soft-fail behavior; and explains that empty sections are a real 'no data' condition, not a bug. This is rich operational context that the annotations alone do not provide.

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 information: return fields, source names, failure modes, and example inputs are all packed in. It could be improved with bulleted structure or a shorter opening example list, but it is dense rather than padded, and the core purpose is front-loaded before the details.

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?

Given the tool's complexity and the fact that there is no output schema, the description is exceptionally complete: it enumerates each returned section, explains sources_used/sources_failed, covers the USPTO sunset, handles the private-company path, and clarifies expected empty results. An agent has enough information to invoke this tool correctly and interpret its response without additional schema support.

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 schema already documents both parameters well. The description adds meaningful value beyond the schema: it explains that `type` values are interchangeable, clarifies that CIK values should be zero-padded, and describes what happens for private companies (resolved:false plus a notes line). Minor redundancy exists, but the added edge-case semantics justify above baseline.

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 ('Tell me about X', 'research Acme') and then states a specific verb+resource: 'full cross-source profile of a US public company in ONE parallel call.' It clearly distinguishes this tool from single-pack SEC/XBRL/news lookups and from siblings like resolve_entity or compare_entities.

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 says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' giving a direct condition for preference. It also covers the private-company edge case, telling the agent that a non-public company returns resolved:false rather than failing, reducing the chance of misinterpreting the result.

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