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

The description goes well beyond the annotations by disclosing fan-out sources, return sections, soft-failure of the USPTO patents source, the meaning of empty sections, and the resolved:false behavior for private companies. It even warns that an empty FDA section is expected for companies with only small-molecule drugs. This is exceptionally transparent.

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 the tool is complex and has no output schema, so the density is justified. It is front-loaded with purpose and usage, then organized by return section, and each clause carries meaningful behavioral or semantic information. Slight redundancy exists around accepted input forms, but it does not harm usability.

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, the absence of an output schema, and the lack of per-source structure elsewhere, the description fully covers what an agent needs to invoke it correctly: input shapes, resolution behavior, return sections, failure semantics, and source status reporting. Nothing critical is missing.

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 baseline is 3. The description adds value by clarifying that names resolve via SEC EDGAR company-name matching, that CIKs should be zero-padded, and that type accepts both 'company' and 'ticker' interchangeably with identical value shapes. These details go beyond the schema's own descriptions.

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 states a specific action: producing a full cross-source profile of a US public company in one parallel call. It clearly distinguishes itself from single-pack SEC/XBRL/news lookups and from other sibling tools by naming the holistic aggregation behavior.

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 to prefer this tool over chaining single-pack SEC/XBRL/news lookups when the user wants a holistic view, and it enumerates accepted input forms. It also states the failure behavior for private companies, helping an agent know when the tool is not applicable. This is explicit when-to-use and when-not-to-use guidance.

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