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

Annotations already mark the tool read-only, open-world, and idempotent, and the description adds substantial behavioral context: it fans out across multiple sources, soft-fails USPTO patents after May 2025, reports fda_products as 'expected, not a failure' when empty, and explains that sources_used/sources_failed distinguish real no-data from bugs. No contradiction with annotations exists.

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 densely packed with necessary detail for a complex tool that has no output schema. It is front-loaded with user intents and the key routing directive, and it groups related behavior (data sources, returns, failure semantics) logically. There is minor redundancy around accepted input forms, but overall every sentence earns its place.

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 that there is no output schema, the description is unusually complete: it enumerates every return section (cik, recent_filings with URIs, fundamentals, patents, contracts, FDA products, hiring, news, LEI, sources_used/sources_failed), explains failure/empty semantics, and covers edge cases like private companies and interchangeable type values. An agent has enough to invoke the tool correctly without additional context.

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%, but the description goes well beyond it: it explains that 'company' and 'ticker' behave identically, gives concrete examples for ticker/CIK/name, notes zero-padding requirements, and describes name resolution via SEC EDGAR plus the resolved_from/resolved_to behavior. This materially helps an agent construct correct values.

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 uses a specific verb ('returns a full cross-source profile') and clearly names the resource ('US public company'), and it opens with concrete user intents. It also distinguishes itself from 'chaining single-pack SEC/XBRL/news lookups' and lists exactly what sections are returned, so an agent can tell it apart from siblings like 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?

It explicitly says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' which directly answers when to use this tool versus alternatives. It also clarifies expected input forms and states that private companies return resolved:false rather than failing, giving the agent clear routing and fallback expectations.

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