Katzilla MCP

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

The description adds valuable behavioral context beyond annotations: it specifies the source ('Local Computation (N/A), updates daily'), describes the return format ('Katzilla envelope { data, quality, citation }'), and explains quality scoring and citation details. Annotations already cover safety (readOnlyHint, destructiveHint) and idempotency, so the description complements them with operational and output behavior without contradiction.

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 well-structured and front-loaded, starting with the core purpose, followed by supported units, source information, and return format. Each sentence adds essential information without redundancy, making it efficient 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 moderate complexity, rich annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It covers purpose, usage context, behavioral traits, and output details, leaving no significant 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 input schema fully documents the three parameters (value, from, to). The description adds minimal parameter semantics by listing example units (e.g., 'km', 'lbs', 'C') in the supported categories, which provides context but does not significantly enhance the schema's details. This meets the baseline 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 ('Convert between common units of measurement') and resources (listing multiple measurement categories like length, weight, volume, speed, temperature). It distinguishes itself from sibling tools (e.g., utilities__fetch-url, utilities__qr-code) by focusing exclusively on unit conversion, making its scope unambiguous.

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 by listing supported measurement categories and units, which implies usage for converting between these specific units. However, it does not explicitly state when not to use it or name alternatives (e.g., for currency conversion or other utilities), leaving some guidance implicit rather than explicit.

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