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

Annotations already mark this as read-only, open-world, and idempotent, and the description adds substantial non-obvious behavior: parallel fan-out across many sources, expected-empty sections for FDA products for non-biologic companies, USPTO sunset soft-failure, sources_used/sources_failed semantics, and resolved:false with notes for private companies. This is exactly the kind of behavioral context annotations do not provide.

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 the tool is genuinely complex with many sources and edge cases, and nearly every clause carries useful operational guidance. It front-loads the purpose and preferred usage before diving into return sections. Slightly better formatting (bullets) would improve scannability, but it is not padded with fluff.

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?

Despite no output schema, the description enumerates the main return sections, explains what empty sections mean, covers accepted input shapes, and handles failure/edge cases like private companies, USPTO sunset, and FDA-licensed biologics. An agent has enough context to invoke the tool correctly and interpret its results in diverse scenarios.

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?

The schema already fully documents both parameters, so the baseline is 3. The description adds meaningful extras: accepted examples for ticker/CIK/name, zero-padding for CIKs, name resolution via SEC EDGAR, and confirmation that type='company' and type='ticker' behave identically. This goes beyond the schema without being redundant.

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 precise purpose: producing a full cross-source profile of a US public company in one call. It includes concrete user phrasings ('Tell me about X', 'brief me on Tesla') and explicitly differentiates itself from chaining single-pack SEC/XBRL/news lookups. This makes the tool's role unambiguous even among siblings like compare_entities and resolve_entity.

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 prefer this tool over chaining individual lookups when the user wants a holistic company view, which is strong when-to-use guidance. It does not enumerate when to choose siblings like deep_research or compare_entities instead, so there is a small gap in exclusion guidance.

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