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 source ('Colormind'), licensing ('Free / No restrictions'), update frequency ('updates daily'), and output structure ('Returns the Katzilla envelope { data, quality, citation }'). Annotations already cover read-only, non-destructive, idempotent, and open-world hints, so the description complements them without contradiction, earning a high score for added transparency.

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 in the first sentence, followed by additional details in a structured manner. Every sentence adds value: the first explains what the tool does, the second details output and source, and the third clarifies the return format. There is no wasted text, making it highly concise and well-structured.

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 optional parameter), rich annotations (read-only, non-destructive, idempotent, open-world), and the presence of an output schema, the description is complete. It covers purpose, source, licensing, update frequency, and output structure, providing all necessary context for an AI agent to use the tool effectively without needing to explain return values, which are handled by the output schema.

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 'model' parameter fully documented in the schema. The description does not add any parameter-specific details beyond what the schema provides, such as examples of model usage or effects on output. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't enhance parameter understanding but doesn't need to compensate for gaps.

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 tool's purpose: 'Generate harmonious color palettes using Colormind's deep-learning model. Returns a 5-color palette with RGB values and hex codes.' It specifies the verb ('Generate'), resource ('color palettes'), method ('Colormind's deep-learning model'), and output format, clearly distinguishing it from sibling tools which focus on different domains like agriculture, consumer data, or entertainment.

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 usage by mentioning 'Source: Colormind (Free / No restrictions), updates daily,' indicating it's a free tool with daily updates, which helps in deciding when to use it. However, it does not explicitly state when not to use it or name specific alternatives among siblings, such as other color-related tools (none are listed in siblings), so it falls short of a perfect score.

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