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

Annotations already mark the tool read-only, idempotent, and non-destructive. The description goes well beyond that by disclosing source fan-out, soft-failure of the USPTO PatentsView sunset, the expected empty 'fda_products' for non-biologic companies, resolved/unresolved behavior for private companies, and the meaning of sources_used/sources_failed. This gives the agent accurate expectations for partial or empty data.

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 the tool is complex and there is no output schema to compensate. It front-loads the intended use and example queries, then systematically lists sources and return fields. Each sentence adds necessary behavioral detail, though it could be tightened with clearer paragraphing.

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?

Given the tool's complexity, no output schema, and rich annotation context, the description covers everything an agent needs: input formats, output fields, per-source behavior, expected empty results, failure modes, and the meaning of resolution failure. This is a complete operational contract for calling the tool 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 valuable meaning: 'type' accepts 'company' or 'ticker' interchangeably, 'value' may be a ticker, zero-padded CIK, or company name, and name resolution happens via SEC EDGAR. It also explains the edge case where a private company yields resolved:false rather than a bare failure, which is genuinely useful 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 states a specific verb and resource: it builds a 'full cross-source profile of a US public company in ONE parallel call.' It distinguishes this tool from chained single-pack SEC/XBRL/news lookups and is clearly not a generic search or a single-data-source fetcher. Example queries reinforce the intended invocation pattern.

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 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' giving a clear when-to-use rule. It also states accepted inputs and private-company behavior, but it does not explicitly contrast with sibling tools like deep_research or compare_entities. This is clear context with no false exclusions, though not a full when-not-to-use list.

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