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

Even with strong annotations, the description adds substantial behavioral context: patents soft-fail after the USPTO API sunset, empty sections are described as real 'no data' rather than bugs, private companies return resolved:false with an explicit notes line, and sources_used/sources_failed clarify partial success. This goes well beyond the read-only/idempotent annotations.

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 and dense, but nearly every sentence adds operational or behavioral detail needed for such a complex multi-source tool. It front-loads example queries and the core purpose before diving into output sections. The interchangeability of type is repeated from the schema, which is mild redundancy in an otherwise well-packed description.

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 fully carries the burden of explaining return values, and it does so thoroughly: cik, resolved_from/resolved_to, recent_filings with URIs, fundamentals ordering, patents sunset, federal contracts, fda_products expected-empty case, hiring, news fallback, LEI, and sources_used/failed. Edge cases such as private companies and empty sections are explicitly covered.

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 description coverage is 100%, so the baseline is 3. The description reinforces the accepted value shapes with examples ('AAPL', '0000320193', 'Moderna') and clarifies that type is effectively interchangeable, but this mostly restates what the schema already documents rather than adding new parameter-specific semantics.

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 defines the tool as a 'full cross-source profile of a US public company in ONE parallel call', so the verb and resource are clear. It distinguishes itself from chaining single-pack SEC/XBRL/news lookups, but it does not explicitly differentiate itself from semantically overlapping siblings like deep_research, compare_entities, or resolve_entity.

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 an explicit trigger: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also states accepted inputs and the failure mode for private companies. However, it does not address when to choose this over sibling tools like compare_entities or deep_research, leaving that comparison implicit.

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