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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive, and the description adds substantial behavior beyond them: USPTO API sunset soft-fail, expected-empty FDA sections for small-molecule companies, GDELT→GNews fallback, sources_used/sources_failed semantics ('an empty section is a real no data, not a bug'), resolved_from/resolved_to behavior, and the private-company notes line. This actively prevents an agent from misinterpreting empty or partial results.

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 trigger phrases and the ALWAYS-PREFER rule, which is the right structure. However, it is a single dense wall of text covering ten-plus return sections with no bullet points, line breaks, or section headers, making it harder for an agent to parse quickly. Every sentence carries information, so it is dense rather than verbose, but the lack of scannable structure costs it a higher score.

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, so the description bears the full burden of explaining return values — and it does, enumerating every data section (cik, recent_filings with URIs, fundamentals with sort order, patents, federal_contracts, fda_products, hiring, news, LEI) plus failure modes and caveats. For a tool this complex (cross-source fan-out across 9+ sources), nothing an agent needs to correctly invoke it and interpret its response 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?

Schema description coverage is 100%, so the baseline is 3, but the description adds concrete example values ('AAPL', '0000320193', 'Moderna'), reinforces that type='company' and type='ticker' are interchangeable, and clarifies the name-resolution path and zero-padded CIK expectation. These are usability enhancements over the schema rather than fully new semantics, which only slightly raises the score 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?

States a specific verb and resource: 'full cross-source profile of a US public company in ONE parallel call.' Multiple trigger phrases ('Tell me about X', 'brief me on Tesla', 'company profile for Microsoft') leave no ambiguity about what request this serves. The holistic cross-source scope distinguishes it from siblings like resolve_entity (single resolution), compare_entities (multi-entity comparison), and single-pack SEC/news lookups.

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?

Provides explicit trigger phrases and a clear preference rule: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also covers the edge case of private companies, which return resolved:false rather than failing. However, it never explicitly says when NOT to use this tool in favor of adjacent siblings like compare_entities or resolve_entity, so exclusion guidance is incomplete.

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