Katzilla MCP

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

Annotations already indicate readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable context beyond annotations: it specifies the data source (Federal Trade Commission), update frequency (daily), and details about the return format (Katzilla envelope with quality scores and citation info), which helps the agent understand data freshness and auditability.

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 front-loaded with the core purpose, followed by source details and return format explanation. Every sentence adds value: the first defines the tool, the second gives source context, and the third clarifies the output structure, with no wasted words.

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 (one optional parameter), rich annotations (read-only, idempotent, open world), and the presence of an output schema (implied by the description of the return format), the description is complete. It covers purpose, source, updates, and output structure, leaving no significant gaps for the agent.

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, with the 'limit' parameter fully documented in the schema. The description does not add any parameter-specific information beyond what the schema provides, so it meets the baseline score of 3 for high schema coverage without extra param semantics.

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 ('Query'), resource ('FTC Do Not Call (DNC) complaint data'), and scope ('reported unwanted call complaints filed with the Federal Trade Commission'), making the purpose specific and clear. It distinguishes from sibling tools like consumer__cfpb-complaints by specifying the FTC DNC dataset.

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: querying FTC DNC complaint data, with source and update frequency mentioned. However, it does not explicitly state when not to use it or name alternatives among siblings, such as consumer__cfpb-complaints for other consumer complaint data.

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