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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. The description adds genuinely useful behavioral context: one parallel call, USPTO soft-fail behavior, FDA small-molecule expected-empty case, resolved:false + notes for private companies, and sources_used/sources_failed semantics. This goes well beyond what annotations provide, though it doesn't document rate limits or latency.

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 information-dense; the trigger examples are front-loaded, then the fan-out list, then return semantics. It earns its length by covering many data-source edge cases and expected failures. It could be tightened with bullets, but the content is purposeful and not padded.

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 the tool's broad fan-out and no output schema, the description covers inputs, resolution behavior, per-source quirks, and failure semantics well. The main gaps are missing names or numbers of sibling tools that would further disambiguate it from deep_research or compare_entities, and no explicit mention of typical latency or upper bound on sources.

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 schema already documents both parameters fully, including the interchangeability of type values and the accepted value shapes. The description reinforces the same semantics and adds the resolved_from/resolved_to nuance for name-based values, but its baseline is the schema doing the heavy lifting.

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-object pair ('full cross-source profile of a US public company'). It names the resource and the fan-out sources, and explicitly contrasts with chaining single-pack lookups, which differentiates it from siblings like deep_research, 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?

It explicitly says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view', gives accepted input shapes, and explains the empty-section semantics and private-company fallback. This gives clear when-to-use and when-not-to-fail guidance.

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