Katzilla MCP

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

Annotations already cover read-only, non-destructive, idempotent, and open-world hints. The description adds valuable behavioral context beyond annotations: it discloses the data source ('openFDA'), update frequency ('daily'), return format ('Katzilla envelope'), quality metrics ('freshness/uptime/confidence'), and audit features ('SHA-256 data hash'). 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 efficiently structured in two sentences: the first states the purpose and source, the second details the return format and audit features. Every sentence adds value, with no wasted words, making it front-loaded and highly 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?

Given the tool's low complexity (1 parameter, 100% schema coverage), rich annotations, and presence of an output schema, the description is complete. It covers purpose, source, update frequency, return format, and audit details, providing all necessary context without needing to explain parameters or return values.

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 has 100% description coverage, fully documenting the 'limit' parameter. The description does not add any parameter-specific information beyond what the schema provides, so it meets the baseline of 3 without compensating or enhancing 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 explicitly states the verb ('Get') and resource ('recent FDA medical device recall reports'), specifies the data source ('openFDA'), and distinguishes it from sibling tools like 'health__fda-recalls' by focusing on devices rather than general recalls. It clearly defines the tool's scope and differentiation.

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 clear context for when to use this tool ('Get recent FDA medical device recall reports'), but does not explicitly mention when not to use it or name specific alternatives among siblings. It implies usage for medical device data, but lacks explicit exclusion guidance.

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