Katzilla MCP

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

The description adds valuable behavioral context beyond annotations: it discloses the daily update schedule, specifies the return format (Katzilla envelope with data/quality/citation), and explains quality metrics (freshness/uptime/confidence) and citation details (source URL, license, SHA-256 hash). While annotations cover read-only/non-destructive/idempotent/open-world aspects, the description enriches understanding of data characteristics and output structure.

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 establishes purpose and source, the second details the return format and its components. Every element serves a clear informational purpose with zero redundant or wasted content, making it easy to parse.

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 moderate complexity, comprehensive annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), complete parameter schema coverage, and existence of an output schema, the description provides excellent contextual completeness. It covers data source, update frequency, return format, and quality/citation details—effectively supplementing the structured metadata without redundancy.

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?

With 100% schema description coverage, the input schema fully documents the single 'limit' parameter. The description adds no parameter-specific information beyond what's in the schema, maintaining the baseline score of 3 where structured data carries the parameter documentation burden.

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 specific purpose: retrieving COVID-19 case surveillance data from the CDC. It specifies the data source (CDC Public Domain), update frequency (daily), and distinguishes itself from siblings by focusing on COVID-19 data rather than other health datasets like CDC WONDER or NIH clinical trials.

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 implies usage for COVID-19 data needs but provides no explicit guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like health__cdc-wonder or differentiate use cases, leaving the agent to infer appropriate contexts without clear boundaries.

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