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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior, so the bar is lower, but the description adds substantial behavioral context beyond annotations: it fans out across multiple sources, returns specific field structures, soft-fails on the USPTO API sunset, reports sources_used/sources_failed, and clarifies that an empty section means real 'no data'. This gives the agent accurate expectations for both success and failure modes.

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 every sentence earns its place by documenting a distinct source, failure mode, or return shape. It is front-loaded with trigger phrases and a clear preference directive, and the detailed enumerations are necessary because there is no 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?

Given no output schema, the description must carry the full burden of explaining return fields and behaviors, and it does. It covers input formats, source coverage, resolution behavior for private companies, expected empty results, failure semantics for the USPTO API, and how sources_used/sources_failed should be interpreted. Nothing an agent needs to invoke this correctly is missing.

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 enriches both parameters with concrete examples ('AAPL', '0000320193', 'Moderna'), explains that names resolve via SEC EDGAR's company-name match, and clarifies that 'type' accepts interchangeable values. This goes meaningfully beyond the schema's own property 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 opens with concrete trigger phrases and a clear verb phrase: 'full cross-source profile of a US public company in ONE parallel call.' It distinguishes itself from chaining single-pack SEC/XBRL/news lookups and from siblings like deep_research and resolve_entity by explicitly stating its single-call holistic scope.

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 says exactly when to use it: when the user asks for a holistic view of a US public company, and explicitly says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups.' It also covers edge cases: private companies return resolved:false with an explicit notes line, and empty data sections are expected, not failures.

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