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

The description goes well beyond the read-only/open-world/idempotent annotations by disclosing important behavioral details: it fans out across many named sources, returns per-source success/failure flags, treats empty sections as genuine 'no data', resolves names and private companies to resolved:false with a notes line, and explains that lack of small-molecule FDA data is expected. This is rich behavioral disclosure that materially improves invocation correctness.

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 extremely dense and runs on in long, semicolon-heavy sentence structures. It contains useful information throughout, but it would benefit from structured bullets or shorter sentences. Some parts, such as the long list of example user phrasings, could be trimmed without losing meaning. It is thorough but not concise.

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?

This is a complex multi-source tool with no output schema, so the description must carry a heavy explanatory burden. It does: enumerating each data source, describing the main return fields, outlining failure semantics, handling private companies, and explaining fallback behavior. An agent has enough context to set expectations and interpret results 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?

The input schema already documents both parameters at 100% coverage, so the baseline is 3. The description adds value by explicitly stating that 'company' and 'ticker' are interchangeable, that value can be a ticker, zero-padded CIK, or company name, and that name resolution happens via SEC EDGAR. This goes beyond the schema without being redundant.

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 clearly identifies the tool's purpose: building a holistic, cross-source profile of a US public company in a single parallel call. It uses specific resource language ('company profile', 'full cross-source profile') and the many example user phrasings make the intent unmistakable. It also implicitly differentiates itself from single-pack lookups and sibling tools like resolve_entity by emphasizing the holistic, multi-source aggregation nature.

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 states when the tool should be preferred: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also clarifies accepted inputs and expected behavior for private companies, which helps an agent decide when not to use it. This is strong, actionable usage guidance.

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