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

The description goes far beyond the read-only annotations by disclosing multi-source fan-out, per-section return semantics, soft-fail behavior for the USPTO PatentsView sunset, empty-section meaning, and the resolved:false path for private companies. It also explains that FDA product absence is expected for small-molecule/generic drug companies, preventing false failure interpretation. This is rich, decision-relevant behavioral context that annotations alone could 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 front-loaded with core purpose and usage preference before moving into detailed output sections. Each detail serves a purpose, especially because there is no output schema to define the return shape. Some repetition exists between the description and the input schema, but for a complex multi-source tool this level of detail is mostly justified rather than wasteful.

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 high complexity, numerous data sources, and lack of an output schema, the description is exceptionally complete. It enumerates every return section, explains failure modes, defines how empty results should be interpreted, and covers private-company behavior. An agent has enough information to invoke the tool correctly and interpret its results without guessing.

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?

Although schema coverage is 100%, the description adds significant semantic value by detailing that type is interchangeable, showing concrete examples for value, specifying zero-padded CIK formatting, and explaining that name resolution goes through SEC EDGAR. It also clarifies that both type values accept the same value shapes, which is behavior not obvious from the schema alone. The description fully compensates for any residual ambiguity in parameter usage.

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 clearly states the tool builds a full cross-source profile of a US public company, with a specific scope and a concrete resource type. It also differentiates itself from the alternative of chaining separate SEC/XBRL/news lookups by explicitly saying ALWAYS PREFER when a holistic view is requested. The many example query phrasings ('tell me about X', 'research Acme') make the intended use obvious without ambiguity.

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?

Usage guidance is explicit: prefer this tool over chaining single-pack SEC/XBRL/news lookups whenever the user wants a holistic view. It also specifies accepted inputs (ticker, zero-padded CIK, company name) and how private companies are handled, plus notes that person/place are not yet supported. This gives an agent clear when-to-use and when-not-to-use signals without needing to infer intent.

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