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

The description goes well beyond the annotations by disclosing parallel fan-out across sources, the semantics of sources_used/sources_failed, that empty sections mean real 'no data' not a bug, name resolution behavior, private company handling, and fallback behavior like GDELT→GNews. This provides rich behavioral context that annotations alone do not convey. No contradiction with annotations exists.

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 front-loaded with query examples and a strong preference rule. It is dense but mostly information-bearing, covering inputs, outputs, sources, and edge cases. It loses a point for being a long single unstructured paragraph with some redundancy with the schema's parameter descriptions.

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 carries full responsibility for explaining what the tool returns. It details each data source, output sections, resolved identities, source success/failure reporting, and input variations. An agent has enough context to invoke the tool correctly and interpret results across many edge cases.

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 value by giving concrete examples, explaining that 'company' and 'ticker' are interchangeable, noting that names resolve via SEC EDGAR, and addressing the private-company resolution case. It slightly repeats schema content but still enriches parameter understanding.

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 a specific verb and resource: building a full cross-source profile of a US public company in one parallel call, with multiple query examples. It is easy to distinguish from siblings like compare_entities or resolve_entity because it explicitly targets a holistic single-company profile.

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?

The description explicitly says to ALWAYS PREFER this tool over chaining single-pack lookups when the user asks for a holistic view. It also clarifies acceptable inputs and edge cases like private companies. However, it does not name specific sibling alternatives or state explicit when-not-to-use conditions, so it falls just short of full guidance.

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