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 return format ('Katzilla envelope { data, quality, citation }'), explains quality scoring ('freshness/uptime/confidence'), and details citation contents ('source URL, license, SHA-256 hash'). Annotations already cover read-only, non-destructive, idempotent, and open-world hints, so the description appropriately supplements rather than contradicts them.

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: first states purpose and resources, second gives usage context and source, third details return format and components. Every sentence adds essential information without redundancy, making it front-loaded and zero-waste.

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 (simple fetch operation), rich annotations (read-only, non-destructive, etc.), 100% schema coverage, and presence of an output schema, the description is complete. It covers purpose, usage, source details, and return format, leaving no gaps for the agent to understand and invoke the tool correctly.

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 schema already fully documents both parameters (resource with enum values and id with omission behavior). The description doesn't add any parameter-specific semantics beyond what's in the schema, such as explaining resource differences or id ranges. This meets the baseline of 3 for high schema coverage.

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 with specific verbs ('fetch mock REST data') and resources ('posts, comments, users, and todos'), and distinguishes it from sibling tools by specifying it's for JSONPlaceholder mock data rather than real-world datasets like agriculture, consumer, or economic data. The mention of 'testing and prototyping' further clarifies its intended use case.

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 ('useful for testing and prototyping') and implicitly suggests alternatives by noting it's for 'mock REST data' (implying real data tools might be better for production). However, it doesn't explicitly name specific sibling tools as alternatives or state when not to use it, keeping it at a 4 rather than a 5.

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