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

Annotations indicate idempotent, read-only, open-world, non-destructive hints. The description adds valuable context beyond these hints: it details that the tool fans out across multiple sources (SEC, XBRL, USPTO, news, GLEIF), explains the patent data situation (sunset May 2025, soft-fails), and specifies return fields (e.g., recent_filings up to 5 with URIs). No contradictions 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 fairly long but well-structured with examples, bullet points, and explicit guidance. Every sentence adds value, though some details (e.g., patent sunset) could be more concise. It is front-loaded with the primary purpose and usage, making it easy to scan.

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 complexity of the tool (multiple sources, varied return fields, limitations), the description is highly complete. It explains the inputs, outputs, data sources, and known limitations (patent sunset, name unsupported). No output schema is present, so the description adequately covers return information.

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 baseline is 3. The description adds meaning beyond the schema: it explains that 'type' is currently only 'company', that 'value' accepts ticker or zero-padded CIK but not names, and provides concrete examples. This extra context is helpful but not exhaustive (e.g., does not specify CIK zero-padding length or case sensitivity).

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: 'full cross-source profile of a US public company in ONE parallel call.' It provides multiple example queries, lists the data sources and returned fields, and distinguishes itself from chaining single-pack lookups. The verb 'profile' combined with the resource 'entity' is specific and action-oriented.

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 tells when to use this tool: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also provides guidance on when not to use it ('names not supported — use resolve_entity first if you only have a name') and suggests an alternative tool. This gives clear context for decision-making.

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