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

With annotations already marking this as read-only, idempotent, and non-destructive, the description still adds substantial behavioral context: it fans out across many named sources, soft-fails on USPTO patents, returns empty sections as genuine 'no data' versus bugs, and exposes `sources_used`/`sources_failed`. It even explains the FDA/biology-narrow scope and the GDELT→GNews fallback, going far beyond the annotations.

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 it is dense and structured: examples first, then the core directive, then a clearly enumerated return layout, then input rules and edge cases. There is minor redundancy with the schema ('type accepts company or ticker interchangeably'), and the wall of semicolon-heavy clauses is hard to scan, but nearly every sentence earns its place given the tool's complexity.

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?

For a high-complexity tool with no output schema, this is unusually complete. It documents all accepted input shapes, enumerates each returned section with example URIs and source caveats, explains expected empty results (FDA biologics, private companies), and covers failure modes via `sources_failed`. An agent has enough context to decide when to call it and how to interpret its result without additional discovery.

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. The description adds value beyond the schema by explaining the zero-padded CIK convention, that names resolve via SEC EDGAR's company-name match, that `type` values are interchangeable in practice, and that private companies resolve to `resolved:false` with an explicit note. This is meaningful contextual enrichment, though the schema already carries much of the parameter 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 natural-language triggers and a crisp statement of what the tool does: 'full cross-source profile of a US public company in ONE parallel call.' The verb is specific ('profile' / 'fan out across sources') and the resource is bounded ('US public company'), which differentiates it from sibling tools like resolve_entity (single resolution) and compare_entities (comparison rather than profile).

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 explicit when-to-use guidance: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also clarifies input flexibility (ticker, CIK, or name), the acceptable `type` values, and how private companies are handled with `resolved:false` and a `notes` line rather than a failure.

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