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

Even with annotations already marking the tool as read-only, idempotent, open-world, and non-destructive, the description adds substantial behavioral detail: the tool fans out across multiple data sources, returns sources_used/sources_failed, soft-fails when USPTO is unavailable, and returns resolved:false with notes for private companies. It also explains that empty sections mean genuine 'no data', not bugs.

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 dense and well-structured, front-loading the core use case and preference rule before detailing fan-out sources and input semantics. Some example queries are redundant, but the length is justified given the tool's complex behavior and lack of an output schema.

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?

For a complex tool with no output schema, the description thoroughly covers return fields, source behavior, failure modes, input resolution, and edge cases like private companies. An agent has enough information to select and invoke the tool correctly without additional external documentation.

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 the baseline is 3, but the description adds meaning beyond the schema by clarifying that 'company' and 'ticker' behave identically, that values can be tickers, zero-padded CIKs, or names, and that names resolve via SEC EDGAR. It also explains what happens for private companies, which is useful for correct invocation.

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 clearly identifies the tool's purpose: producing a full cross-source profile of a US public company in one call, with concrete example queries. It also distinguishes itself from chaining single-pack SEC/XBRL/news lookups, which helps an agent differentiate it from related sibling 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?

The description explicitly says to prefer this tool over chaining single-pack lookups when the user asks for a holistic view, and provides accepted input shapes. It doesn't explicitly name when to use alternative siblings like resolve_entity or compare_entities, but the guidance is clear enough for most cases.

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