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

Beyond the read-only/idempotent annotations, the description discloses multi-source fan-out, parallel execution, soft-fail behavior (USPTO sunset), the meaning of empty sections vs bugs, and private-company resolved:false behavior. This adds real behavioral context rather than repeating 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 first sentence is a good front-loaded hook, but the description then becomes a dense, run-on paragraph listing return fields and edge cases. Every sentence is informative for a complex tool, yet the lack of bullet structure makes it hard to scan.

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 full burden for explaining return values, and it does so thoroughly: it lists output fields, source failure signaling, name resolution semantics, and expected-empty cases. The agent has enough to invoke and interpret the call correctly.

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 baseline is 3; the description adds meaningful nuance beyond the schema by stating that type values are interchangeable, showing example ticker/CIK/name formats, and explaining SEC EDGAR name resolution. This exceeds baseline but still mostly restates parameter concepts present in the schema.

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 clearly states it builds a full cross-source profile of a US public company, with concrete user-phrase examples ('Tell me about X', 'brief me on Tesla'). It does not explicitly differentiate from sibling tools like compare_entities or resolve_entity, only from chaining single-pack lookups, so it misses the top bar for sibling differentiation.

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 gives an explicit directive: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' plus accepted input shapes. It stops short of saying when not to use the tool versus named siblings such as compare_entities, so guidance is strong but not exhaustive.

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