Katzilla MCP

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

Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond this: it specifies the data source (Eurostat), update frequency (monthly), return format (Katzilla envelope with data, quality, citation), and details about quality scores and citation contents (URL, license, SHA-256 hash). This enriches the agent's understanding 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 covers purpose, source, and context; the second details the return format and its components. Every element adds value without redundancy, making it front-loaded and 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, rich annotations (read-only, idempotent, etc.), 100% schema coverage, and the presence of an output schema (implied by the return format description), the description is complete. It covers purpose, usage context, behavioral details, and output structure, leaving no significant gaps for the agent.

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, fully documenting the 'geo' and 'coicop' parameters with defaults and enums. The description does not add any parameter-specific semantics beyond what the schema provides, such as explaining the significance of geo codes or COICOP categories in context. Thus, it meets the baseline but does not enhance parameter understanding.

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 retrieves Harmonised Index of Consumer Prices (HICP) inflation data from Eurostat, specifying it's monthly data for EU/EEA countries and the standard measure for ECB policy. It distinguishes itself from siblings like economic__eurostat-gdp or economic__eurostat-unemployment by focusing on inflation metrics.

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 (ECB monetary policy decisions, monthly updates) and implies when to use it (for inflation data vs. other economic indicators). However, it does not explicitly state when not to use it or name specific alternative tools for similar data, which prevents a perfect score.

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