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

Annotations already provide readOnly, openWorld, idempotent, and non-destructive hints, but the description adds substantial behavioral context: it fans out across multiple sources in parallel, soft-fails on the USPTO API sunset, reports sources_used/sources_failed, and explicitly states that empty sections are real 'no data' rather than failures. It also explains the resolved:false behavior for private companies.

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 and dense, but every sentence carries meaningful information: usage examples, source list, output fields, failure modes, and parameter guidance. It is front-loaded with user phrasing examples and prioritized usage. The run-on structure is slightly overwhelming, but for a tool of this complexity it is appropriately thorough rather than bloated.

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?

Despite having no output schema, the description enumerates the full response shape (cik, company_name, recent_filings with URIs, fundamentals, patents, federal_contracts, fda_products, hiring, news, LEI, sources_used/failed), explains each section's meaning, and covers expected failure modes. For a high-complexity tool, this leaves no critical gap an agent would need to invoke it 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 description coverage is 100%, and the schema already documents both parameters and the interchangeable type enum. The description adds value by giving concrete examples (AAPL, 0000320193, Moderna), clarifying that names resolve via SEC EDGAR's company-name match, and describing the edge-case behavior when the value is a name that cannot be resolved.

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 states a specific purpose: building a full cross-source profile of a US public company in one call, with examples like 'research Acme' and 'brief me on Tesla'. It clearly distinguishes itself from chaining single-pack SEC/XBRL/news lookups and lists the exact data domains returned.

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 ALWAYS PREFER this tool over chaining single-pack lookups when the user asks for a holistic view. It also explains when it will not fully succeed (private companies return resolved:false, small-molecule-only drug companies show no fda_products), giving clear expectations for alternative scenarios.

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