Katzilla MCP

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

Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond this: it specifies the data source ('U.S. Consumer Product Safety Commission'), update frequency ('updates daily'), and details about the return format ('Katzilla envelope { data, quality, citation }') with explanations of quality scores and citation components. This enriches understanding without contradicting 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 three sentences: it states the purpose, provides source and update info, and explains the return format. Each sentence adds essential information without redundancy, making it front-loaded and easy to parse for an AI agent.

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 complexity (search with parameters), rich annotations (read-only, idempotent, etc.), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers purpose, source, update frequency, and return structure, leaving detailed output to the schema. No gaps are evident for effective tool use.

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, clearly documenting the 'query' and 'limit' parameters. The description does not add any additional meaning or examples beyond what the schema provides (e.g., it doesn't elaborate on search syntax or result ordering). With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but doesn't need to.

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: 'Search CPSC product safety violations and section 15 reports for hazardous consumer products.' It specifies the verb ('Search'), resource ('CPSC product safety violations and section 15 reports'), and distinguishes it from sibling tools like 'consumer__cpsc-recalls' by focusing on violations and reports rather than recalls.

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: searching for hazardous consumer products via CPSC data. It mentions the source and update frequency ('updates daily'), which aids in timing decisions. However, it does not explicitly state when not to use it or name alternatives among siblings (e.g., 'consumer__cpsc-recalls'), missing full differentiation.

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