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

Annotations already declare this as read-only, idempotent, and non-destructive, and the description adds substantial behavioral detail beyond that: it fans out across many sources, reports `sources_used` and `sources_failed`, soft-fails on the USPTO sunset, and returns `resolved:false` with notes for private companies. It also sets expectations that FDA data may legitimately be empty depending on company type.

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 earns its length: it front-loads the core intent and usage preference, then systematically enumerates sources, return fields, and failure semantics. The structure is organized enough that each segment adds necessary context for correct invocation.

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 present, the description carries the full burden of explaining return values and edge cases. It lists all major response components, failure modes, source fallbacks, and input resolution behavior. An agent has enough context to select and call the tool correctly without further inference.

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?

While the schema already documents both parameters, the description adds meaning by showing that `type` and `value` are interchangeable, listing accepted forms (ticker, zero-padded CIK, company name), and explaining resolution behavior and private-company output. This goes beyond the schema's basic field descriptions.

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 explicitly states the tool produces a 'full cross-source profile of a US public company in ONE parallel call' and grounds this with concrete example queries like 'brief me on Tesla'. It clearly differentiates the tool from chaining single-purpose lookups and from sibling tools such as compare_entities by focusing on entity profiles.

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 gives an explicit selection rule: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also explains what input shapes are valid, how private companies are handled, and when an empty section is expected rather than an error.

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