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

Annotations already cover safety (read-only, idempotent, non-destructive), and the description adds substantial behavior beyond that: parallel fan-out across external sources, soft-fail on USPTO, expected-empty FDA sections, sources_used/sources_failed, and private-company resolved:false behavior. It even prevents misinterpretations of empty data. No contradiction with annotations.

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 length is earned by the tool's breadth: examples, source fan-out, return fields, caveats, and input modes. It front-loads the core behavior and preference rule before diving into detail. A few example strings and the final parameter note are redundant with the schema, keeping it just shy of a 5.

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 there is no output schema, the description compensates by enumerating every return bucket (cik, recent_filings, fundamentals, patents, federal_contracts, fda_products, hiring, news, LEI, sources_used/failed) plus edge-case behavior. For a high-complexity tool, nothing essential an agent needs to confidently invoke it 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?

The input schema covers both parameters at 100% and already documents ticker/CIK/name inputs and type interchangeability. The description repeats essentially the same parameter semantics, adding only output-context notes like resolved_from/resolved_to. Since the schema carries the full parameter documentation burden, the baseline 3 is appropriate.

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 example queries and immediately defines the operation: a single parallel call that returns a full cross-source profile of a US public company. It explicitly contrasts itself with chaining single-pack lookups, so an agent can tell it apart from narrower research tools. The enumeration of return sections and accepted identifiers makes the tool's scope unmistakable.

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 an explicit preference rule: ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view. It also gives behavioral guidance around private companies, name resolution, and expected-empty sections. It lacks a formal 'don't use for X' list, but the main selection context is clearly established.

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