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

Annotations already signal readOnly, openWorld, idempotent, and non-destructive behavior. The description goes far beyond this: it lists all data sources, discloses that USPTO may soft-fail after the API sunset, explains that empty FDA products is expected for non-biologics, notes that empty sections are 'a real no data, not a bug,' and describes the resolved:false path for private companies. This is rich behavioral context that lets an agent interpret results correctly.

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 nearly every sentence carries operational detail: source list, return field semantics, input forms, and edge cases. It is front-loaded with intent examples and the one-sentence purpose. It could be slightly better structured (e.g., bulleted lists) to reduce cognitive load, but it is appropriately sized for a multi-source tool and avoids redundancy.

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 a 2-parameter schema, no output schema, and rich annotations, the description is remarkably complete. It specifies the exact input shapes, the full return structure with field names, behavior for no-data sections, failure modes (USPTO soft-fail, private company resolution), and the source list. An agent has everything needed to invoke the tool correctly and interpret its response.

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%, which earns a baseline of 3. The description adds meaningful extra semantics: 'type accepts company/ticker interchangeably,' 'value can be a ticker, CIK, or company name either way,' and 'names now resolve via SEC EDGAR's company-name match.' It also notes that person/place support is coming soon, which clarifies limitations beyond the schema.

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 query examples and immediately states the core function: 'full cross-source profile of a US public company in ONE parallel call.' It also explicitly contrasts with 'chaining single-pack SEC/XBRL/news lookups,' making it clear this is the holistic research tool and distinguishing it from more targeted sibling tools 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?

The description gives a clear directive: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' This tells the agent when to choose this tool over a series of lookups. However, it does not explicitly exclude it for narrower requests or name alternatives like resolve_entity or compare_entities, so the guidance is strong but not exhaustive.

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