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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description goes beyond that by explaining the parallel fan-out across many sources, the soft-fail behavior of the USPTO patents source, the expected absence of FDA products for companies with only small-molecule drugs, and the explicit 'no data, not a bug' semantics for empty sections.

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 and dense, but almost every sentence carries operational value: example queries, routing guidance, source enumeration, return-field semantics, and failure interpretation. It is front-loaded with the core purpose and preference rule, though a bit of internal repetition (input shape details appearing twice) keeps it from a perfect score.

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 complex tool with no output schema and 2 params, the description covers all key aspects an agent needs: accepted input shapes, multi-source behavior, specific return sections, fallback mechanisms, source status fields, and expected edge cases like private companies and empty FDA data. Nothing critical 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 baseline is 3, but the description adds meaningful semantics: it explains that 'type' accepts 'company' or 'ticker' interchangeably, gives concrete example values for 'value', and clarifies name resolution via SEC EDGAR plus the resolved_from/resolved_to behavior. This enriches what the schema alone provides.

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 a specific verb and resource: produce a full cross-source profile of a US public company in one parallel call. It gives concrete example queries ('Tell me about X', 'brief me on Tesla') and explicitly contrasts itself with chaining single-pack lookups, making sibling differentiation immediate.

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 states 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' giving an explicit selection rule versus alternatives. It also defines acceptable input shapes (ticker, CIK, name), what happens for private companies, and how to interpret empty sections as real 'no data' rather than failures.

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