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

Annotations already declare readonly/idempotent, so the description's burden is behavioral detail beyond safety. It adds substantial context: parallel fan-out across SEC, XBRL, patents, contracts, FDA, hiring, news, and GLEIF; explicit mention of sources_used/sources_failed; and the warning that an empty section is real 'no data' not a bug. This is exactly the kind of behavior an agent needs to interpret results correctly.

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 information-dense; every sentence contributes actionable detail about purpose, sources, return fields, and edge cases. It is front-loaded with the core purpose and preferred usage. The structure is a single dense paragraph, so splitting into bullet points would improve scannability, but it earns its length 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?

Even without an output schema, the description covers return fields (cik, company_name, recent_filings with URIs, fundamentals sections, sources_used/sources_failed), source-by-source detail, and failure modes (private company, no data for a source). This is sufficient for an agent to know what results to expect and how to handle anomalies. The tool is complex, and the description addresses that complexity thoroughly.

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 giving concrete examples ('AAPL', '0000320193', 'Moderna'), clarifying that type is interchangeable for all value shapes, and explaining name resolution via SEC EDGAR. It does not need to compensate for schema gaps, but it enriches parameter understanding.

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 states the tool's purpose: a 'full cross-source profile of a US public company in ONE parallel call.' It uses actionable verbs like 'tell me about X' and explicitly contrasts itself with chaining single-pack lookups. It distinguishes itself from sibling tools such as resolve_entity or compare_entities by emphasizing the holistic cross-source 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?

Usage guidance is explicit: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also specifies accepted input forms (ticker, CIK, company name) and explains what happens for private companies (resolved:false with notes). This leaves minimal ambiguity about when to invoke the tool.

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