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

The description goes far beyond the readOnlyHint/openWorldHint/idempotentHint annotations. It discloses important behavioral nuances: 'an empty section is a real "no data", not a bug', 'a private company ... returns resolved:false with an explicit notes line, not a bare failure', and 'sources_used / sources_failed say which of these actually returned data for THIS company'. It also reveals fallback behavior (GDELT→GNews), resolved_from/resolved_to mechanics, and expected empty results for small-molecule-only companies. This is rich behavioral transparency.

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 carries substantive information, from usage examples to source enumeration to edge-case behavior. It is front-loaded with purpose and the key 'ALWAYS PREFER' directive. However, it is structured as one dense run-on paragraph with heavy semicolon use, which harms scannability. Given the tool's complexity, the length is justified, but clearer formatting would make it 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?

With no output schema, the description has to explain return values, and it does so thoroughly: cik, company_name, recent_filings with URIs, fundamentals, patents, federal contracts, FDA products, hiring, news, LEI, and sources_used/sources_failed. It also covers important failure modes (private company, invalid ticker, empty sections) and input variations. For a complex tool with 2 parameters and many data sources, this description leaves no major gap an agent would need filled to call 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% — both `type` and `value` are already thoroughly described in the schema with examples and resolution rules. The description largely restates the same parameter semantics ('Pass a ticker, zero-padded CIK, or a company name', 'type accepts company or ticker interchangeably') without adding new input-level meaning beyond the schema. Baseline 3 is appropriate because the schema already carries the full parameter documentation burden.

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 example queries ('Tell me about X', 'research Acme', 'brief me on Tesla') and then states the core purpose: 'full cross-source profile of a US public company in ONE parallel call'. This clearly identifies the verb, resource, and scope. It also distinguishes from sibling tools by explicitly contrasting with 'chaining single-pack SEC/XBRL/news lookups', so an agent knows this is the holistic profile tool.

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 a strong directive: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' This provides clear context for when to use the tool and names the alternative pattern to avoid. However, it does not explicitly say when NOT to use this tool (e.g., for a simple fact lookup) or name other sibling tools like compare_entities or resolve_entity as alternatives, leaving some exclusion boundaries implied rather than explicit.

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