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

Annotations already declare readOnlyHint and idempotentHint, but the description goes far beyond them: it discloses multi-source fan-out, the USPTO API sunset soft-fail, empty-section semantics ('an empty section is a real no data, not a bug'), and the resolved:false contact for non-public companies. 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 efficiently organized: trigger examples, purpose, usage preference, source list, return-section summary, then edge cases. Every sentence adds operational value; no redundant filler. It is slightly verbose relative to simpler tools, but the complexity of the fan-out behavior justifies the length and the front-loading is strong.

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 delivers: it lists all major response sections, defines how sources_used/sources_failed behave, covers input edge cases, and clarifies expected results for companies with no FDA biologics. An agent can predict the tool's behavior and interpret results correctly without additional documentation.

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 schema already fully documents both parameters. The description adds clarifying semantics: type accepts 'company' or 'ticker' interchangeably and value can be a ticker, CIK, or name — going beyond the schema's enum by explaining behavioral equivalence and value-shape flexibility. This extra context justifies a score above the baseline 3.

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 names an explicit verb-resource pair ('full cross-source profile of a US public company') and opens with concrete trigger phrases like 'research Acme' and 'brief me on Tesla'. It distinguishes itself from sibling single-pack lookups by asserting 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups', making its unique role immediately clear.

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?

Provides an explicit preference rule ('ALWAYS PREFER over chaining single-pack... lookups when the user asks for a holistic view') and specifies accepted input forms (ticker, zero-padded CIK, or company name). It also pre-empts misuse by explaining that a private company returns resolved:false with an explicit notes line, so an agent knows how to handle out-of-scope requests.

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