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

Annotations only cover read-only/idempotent safety; the description adds substantial runtime behavior: parallel fan-out across SEC EDGAR, XBRL, USPTO, USAspending, Purple Book, DOL LCA, GDELT/GNews, and GLEIF; patents soft-fail; an empty FDA section is expected; `sources_used`/`sources_failed` indicate real no-data; and private companies return resolved:false with notes. This makes call outcomes highly predictable.

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 dense and organized, with the core capability front-loaded and return sections enumerated with semicolons. However, it is verbose: seven near-synonymous example phrasings and some redundancy with the schema keep it from a 5.

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?

There is no output schema, and the description compensates by enumerating every major return block (recent_filings, fundamentals, patents, federal_contracts, fda_products, hiring, news, LEI) plus failure and empty-result semantics. For a tool with this many sources and edge cases, nothing essential to invoking it 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?

The input schema already covers both parameters at 100%, so the baseline is 3. The description adds concrete formats: zero-padded CIK ('0000320193'), company-name resolution via SEC EDGAR, and the explicit note that 'company' and 'ticker' are interchangeable. That extra nuance justifies 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?

The opening line states the core function: 'full cross-source profile of a US public company in ONE parallel call.' The examples ('brief me on Tesla', 'what does Apple do') map directly to user intent, and the contrast with 'chaining single-pack SEC/XBRL/news lookups' distinguishes it from narrower alternatives.

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,' which gives a strong when-to-use trigger and the alternative pattern to avoid. It does not enumerate exclusions or compare directly with sibling tools like resolve_entity or compare_entities, so it is not a perfect 5.

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