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

Despite readOnlyHint=true, the description adds substantial behavioral detail beyond annotations: the exact fan-out destinations, the USPTO PatentsView API sunset and soft-fail behavior, the FDA section's expected-empty caveat for small-molecule/generic-only companies, and the meaning of sources_used/sources_failed. It also explains resolved:false behavior for private companies, giving agents a complete operating picture.

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 densely informative, and it is front-loaded with the core value proposition and usage rule before enumerating output fields. The return-section breakdown is necessary because there is no output schema. Minor redundancy with the schema's parameter descriptions keeps it from a 5, but every sentence earns its place.

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 two required params, no output schema, and many possible sub-sources, the description covers input shapes, accepted value forms, per-section return contents, failure modes, expected-empty sections, deprecated sources, and the private-company behavior. An agent has enough context to invoke it correctly and interpret any result.

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 description coverage is 100%, and both parameters are well documented with examples in the schema. The description repeats and elaborates on those examples ('AAPL', zero-padded CIK, 'Moderna') but does not add meaningfully new parameter semantics beyond what the schema already provides. 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 query patterns ('Tell me about X', 'research Acme', 'company profile for Microsoft') and states a specific, differentiating outcome: 'full cross-source profile of a US public company in ONE parallel call.' It names the resource (US public company) and the verbs (profile, brief, research), and the fan-out detail in the return fields further distinguishes it from generic research tools.

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 instructs 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' giving a clear when-to-use rule. It also covers the private-company edge case ('returns resolved:false with an explicit notes line, not a bare failure'), telling agents when the tool partially succeeds and how to interpret it.

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