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

Annotations already declare readOnly/openWorld/idempotent, and the description adds substantial behavioral context far beyond that: parallel fan-out across sources, soft-failure behavior for USPTO, FDA sections expected to be empty for some companies, source_used/source_failed semantics, and private-company fallback behavior. No contradictions with 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 and dense, but nearly every sentence carries necessary behavioral or usage information for a complex multi-source tool. It is front-loaded with example queries and the preferred-use directive. The main downside is that it is one sprawling paragraph without formatting that would aid scanning.

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 having no output schema, the description thoroughly documents every major output field, edge cases, empty-result semantics, source failures, and acceptable input forms. An agent has enough context to invoke the tool correctly and interpret results across a wide range of user questions.

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 baseline is 3. The description adds genuine value beyond the schema: type values are interchangeable, both parameter shapes accept all three input forms, and name resolution via SEC EDGAR is explained. This elevates it above baseline.

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?

States a specific verb-resource pairing: producing a full cross-source profile of a US public company in one call. Example queries illustrate intent and make clear it is a holistic research tool, distinguishing it from single-pack lookups and sibling tools like compare_entities or 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?

Explicitly says ALWAYS PREFER this over chaining single-pack SEC/XBRL/news lookups when a holistic view is needed. It also clarifies what happens for private companies and name-based lookups, giving an agent clear decision rules for when this tool is appropriate.

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