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

The description goes well beyond the readOnly/idempotent annotations by disclosing parallel fan-out, per-source fallbacks (GDELT→GNews), soft-fail behavior, and the meaning of 'sources_used'/'sources_failed' including that empty sections indicate real absence of data. It also explains private-company behavior with resolved:false and a notes line. These are genuinely useful behavioral details not visible from annotations alone.

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 front-loaded with purpose and trigger phrases, but it quickly becomes a dense wall of text with multiple parenthetical lists and repeated input-shape details already present in the schema. It contains a lot of valuable detail, but it would benefit from bullet points or clearer segmentation, and some example phrases are redundant.

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?

With no output schema, the description carries the burden of explaining return semantics, and it does so thoroughly: CIK/company name, resolved_from/resolved_to, recent_filings URIs, fundamentals, patents, federal contracts, FDA biologics, H-1B data, news fallback, LEI, and source success/failure reporting. It also covers input edge cases, private companies, and soft-fail behavior, making it effectively complete for an agent to invoke correctly.

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 meaningful parameter nuance: examples of ticker, zero-padded CIK, and company name, plus the note that 'type' accepts 'company' or 'ticker' interchangeably and that names resolve via SEC EDGAR. It also clarifies the private-company outcome, which is not in the schema. This is a clear value-add beyond the schema.

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 states the tool's function: 'full cross-source profile of a US public company in ONE parallel call.' It names the resource (a US public company), the verb (profile/brief/research), and the cross-source fan-out behavior. It also distinguishes itself from chained single-pack SEC/XBRL/news lookups by saying 'ALWAYS PREFER' this tool for a holistic view.

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 gives explicit when-to-use guidance with trigger examples and says to prefer this over chaining single-pack lookups for a holistic view. It also clarifies scope ('US public company') and edge cases like private companies and name resolution. However, it does not explicitly contrast with sibling tools such as compare_entities or resolve_entity, so the routing guidance is strong but not exhaustive.

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