Katzilla MCP

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies the return format (JSON-stat), update frequency (monthly), and the structure of the response envelope ({ data, quality, citation }) with details on quality scores and citation contents. This enhances understanding of the tool's behavior without contradicting annotations.

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 states the purpose and scope, and the second details the return format, update frequency, and response envelope. Every sentence adds essential information without redundancy, making it front-loaded and concise.

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 complexity (querying a statistical database with multiple parameters), the description is complete. It covers purpose, usage context, behavioral traits (like update frequency and response structure), and there is an output schema (implied by 'Has output schema: true'), so return values need not be explained. The annotations and schema provide additional structured information, making the description well-rounded.

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 clear documentation for 'dataset', 'geo', and 'time' parameters. The description does not add specific semantics beyond what the schema provides, such as examples or constraints not in the schema. Given the high schema coverage, the baseline score of 3 is appropriate, as the description relies on the schema for parameter details.

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: 'Query Eurostat for European statistical data including GDP, population, employment, trade, and more.' It specifies the verb ('query'), resource ('Eurostat'), and scope ('European statistical data'), and distinguishes it from sibling tools like 'demographics__census-acs' or 'economic__eurostat-gdp' by covering a broader range of data types beyond just demographics or specific economic indicators.

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: it's for querying Eurostat data, returns JSON-stat format, updates monthly, and includes quality and citation information. However, it does not explicitly state when to use this tool versus alternatives like 'economic__eurostat-gdp' or 'trade__eurostat-trade', which are more specific sibling tools. The guidance is implied but not explicit about exclusions or direct comparisons.

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