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

Annotations already indicate safe reading (readOnlyHint, idempotentHint, not destructive). The description adds significant behavioral context: fans out across multiple sources (SEC, XBRL, USPTO, news, GLEIF), returns specific fields, and notes that patent data 'soft-fails until reactivated.' No contradiction with 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 a single dense paragraph covering many details. While comprehensive, it could be better structured (e.g., bullet points or separate sections). It front-loads the purpose with examples, which is good, but the length reduces scannability.

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?

Despite lacking an output schema, the description thoroughly explains the return data: CIK, company name, recent filings with URIs, fundamentals, patents, news, and LEI. It covers input constraints, data sources, and fallback behavior, making the tool's behavior fully understandable.

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% (both parameters described in schema). The description adds meaning beyond schema by specifying that 'type' only supports 'company' and 'value' accepts ticker or zero-padded CIK, and clarifying what formats are valid. This provides practical usage hints.

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 provides a 'full cross-source profile of a US public company in ONE parallel call.' It lists specific query examples and distinguishes itself from chaining single-pack lookups by saying 'ALWAYS PREFER over chaining.' This makes the purpose unmistakable.

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 when to use this tool: 'when the user asks for a holistic view.' It also tells when not to use it: if only a name is provided, stating 'names not supported (use resolve_entity first if you only have a name).' This provides clear decision-making guidance.

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