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

Annotations already indicate read-only, idempotent, open-world behavior. The description adds substantial behavioral detail: sources_used/sources_failed indicate real no-data vs bugs, empty sections are expected for certain company types, private companies return resolved:false with notes, and names resolve via SEC EDGAR. This goes far beyond the 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 densely packed with high-value specifics, edge cases, and supported inputs. It front-loads the core purpose and preference rule, then systematically lists returned sections and failure semantics. Some redundancy with the schema exists, but the complexity of the tool justifies the length.

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 the full burden of explaining return values and success/failure modes. It enumerates all returned data categories, source fallback behavior, accepted input shapes, and the private-company edge case. Nothing essential for invoking the tool correctly 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%, but the description adds practical meaning: accepted value formats include ticker, zero-padded CIK, and company name; names resolve via SEC EDGAR; private companies yield resolved:false. The type parameter's interchangeability is also reinforced with examples, adding confidence beyond the enum alone.

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 uses specific verbs ('profile', 'brief', 'research') and clearly states the resource: a full cross-source profile of a US public company. It distinguishes itself from chaining single-pack lookups and from sibling tools like compare_entities or resolve_entity by emphasizing the holistic, parallel, one-call nature.

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 says to prefer this tool when the user asks for a holistic view and to avoid chaining single-pack SEC/XBRL/news lookups. It also explains when a private company returns resolved:false. It doesn't explicitly name all alternative tools or say when NOT to use it, but the main usage context is clear.

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