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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the description's behavioral burden is reduced. It adds valuable context about data source and accessibility: 'No auth — US Consumer Product Safety Act public domain data, unlimited free.' It also states the return fields, which helps set expectations. 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 concise and well-structured, starting with the core purpose, then providing examples, return details, and use cases. Every sentence adds value, though it could be slightly tighter by merging the use-case and data-source statements. It is not overly verbose.

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?

The description covers the essential context: what the tool returns, typical use cases, authentication requirements, and scope. An output schema is present, so detailed return structure is not required in the description. It does not mention pagination limits beyond the schema's 'limit' parameter, but that is adequately covered by the schema.

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 description coverage is 100%, so parameters like manufacturer, limit, date_start, and date_end are fully documented in the schema. The description adds no parameter-specific details beyond mentioning 'optional date range filtering,' which is already captured in the schema. Baseline 3 is appropriate.

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 a specific verb and resource: 'Retrieve all CPSC consumer product recall notices for a specific manufacturer or brand name.' It gives concrete examples (Samsung, Polaris, Kidde, IKEA) and mentions distinct use cases. It does not explicitly contrast with sibling tools like cpsc.safety.search, but the manufacturer-specific scope is unambiguous from the name and phrasing.

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 provides strong context for when to use this tool: 'Essential for supply chain due diligence, vendor safety audits, product liability research, and regulatory compliance checks on specific manufacturers or brands.' It implies that the tool is for manufacturer/brand-specific queries rather than general recall search, but it does not explicitly state exclusions or name alternative tools for broader searches.

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