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

Annotations already mark it read-only, idempotent, and non-destructive, and the description meaningfully builds on that by disclosing parallel fan-out, soft-failure of the USPTO patents source, the meaning of sources_used/sources_failed, and the resolved:false behavior for private companies. It also preempts misinterpretation by stating an empty fda_products list is expected, not a bug. This is rich behavioral context beyond what annotations convey.

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 with many sources, return fields, and edge cases. It is front-loaded with user-intent examples and the precedence rule before the detailed source breakdown. While it could be tightened, each section earns its place by explaining behavior an agent needs to invoke the tool correctly.

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?

For a two-parameter tool with no output schema, the description is exceptionally complete: it covers all accepted input forms, the parallel source architecture, each return section, failure semantics, and how to interpret empty results. It even addresses error-like scenarios (private company, patent soft-fail) so an agent can set expectations. Nothing essential for correct invocation 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 schema already documents both parameters. The description adds value by providing concrete examples for each accepted value shape ('AAPL', '0000320193', 'Moderna'), clarifying that type values are interchangeable, and noting that names resolve via SEC EDGAR's company-name match. This goes beyond bare schema text, though the schema already carries much of the meaning.

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', 'brief me on Tesla') and immediately defines the resource: 'full cross-source profile of a US public company in ONE parallel call'. It enumerates the exact sources and returned fields, and the 'ALWAYS PREFER over chaining single-pack...' line distinguishes it from narrower lookup tools. This leaves no ambiguity about what entity_profile does.

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, which is clear when-to-use guidance with a named alternative. It also explains that private companies return resolved:false rather than a bare failure and that empty drug sections are expected. However, it does not explicitly contrast with sibling tools like deep_research or compare_entities, so exclusion guidance is incomplete.

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