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

Beyond the readOnly/idempotent annotations, the description discloses important behavioral nuances: fan-out across many sources, expected empty sections for fda_products, the meaning of sources_used/sources_failed, and the resolved:false response for private companies. This adds substantial context beyond the annotations and contains no contradictions.

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 and dense, but every sentence earns its place given the tool's multi-source complexity. It is front-loaded with purpose and examples, then systematically details return fields and edge cases. Minor formatting improvements (line breaks) would aid readability, but the structure is logical.

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. It does so thoroughly: listing all major result sections, their content limits, the sources_used/sources_failed contract, and input resolution behavior. This is complete for a tool of this complexity.

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 meaning beyond the schema: type values are explicitly interchangeable, names resolve via SEC EDGAR company-name match, and private companies yield resolved:false. This enriches the parameter understanding beyond the raw 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 states a specific verb and resource: 'full cross-source profile of a US public company in ONE parallel call.' It opens with concrete user phrasings and explicitly distinguishes itself from chaining single-pack lookups, so an agent can clearly separate it from sibling 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 provides explicit when-to-use guidance: prefer this over single-source lookups when the user asks for a holistic view. It also details acceptable inputs (ticker, CIK, company name) and handles the private-company edge case, giving the agent clear routing rules.

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