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

Annotations already mark this read-only, idempotent, and non-destructive, and the description adds substantial behavioral context: it fans out across specific sources, soft-fails on the USPTO PatentsView API sunset, treats empty sections as real 'no data,' and explains that private companies return resolved:false with a notes line. It also discloses that FDA products are limited to biologics, so absence of small-molecule drugs is expected.

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 every section earns its place given the tool's complexity: example phrases, source fan-out, per-section return semantics, failure modes, and input resolution rules. The core use case is front-loaded with query examples, and while dense, the semicolon-separated structure keeps related details grouped.

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 carries the full burden of explaining return values, and it does so comprehensively: it lists every response section, explains sources_used/sources_failed, covers expected empty results, and specifies the private-company failure path. An agent has enough context to call this tool correctly and interpret results without ambiguity.

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?

The input schema already provides 100% coverage for both parameters, including the interchangeability of type and the accepted value shapes. The description adds value by giving concrete examples, emphasizing zero-padded CIKs, and noting name resolution via SEC EDGAR, but much of this repeats or lightly extends the schema. It is additive, but not transformative.

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's verb and resource: it produces a 'full cross-source profile of a US public company in ONE parallel call.' It enumerates the exact data sources and explicitly frames itself against the alternative of chaining single-pack SEC/XBRL/news lookups, making it easy to distinguish from siblings like deep_research 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 gives an explicit, imperative usage rule: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also provides clear conditions around accepted inputs, private-company resolution, and the meaning of empty sections. This is actionable guidance for when to invoke this tool versus a lookup chain.

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