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 data source ('Eurostat'), update frequency ('updates monthly'), and return format ('Katzilla envelope { data, quality, citation }' with details on quality scores and citation components). Annotations cover read-only, non-destructive, idempotent, and open-world hints, but the description enhances this with practical usage insights like data freshness and audit features.

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 and efficiently structured: it starts with the core purpose, adds key details (coverage, source, updates), and ends with return format specifics. Every sentence adds value without redundancy, making it easy to scan and understand quickly.

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 (trade data retrieval), rich annotations (read-only, idempotent, etc.), 100% schema coverage, and presence of an output schema, the description is complete enough. It covers purpose, data scope, source, update frequency, and return format, addressing key contextual needs without needing to explain parameters or output details already handled by structured fields.

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 all three parameters (geo, partner, indic_et) with descriptions and enums. The description does not add significant parameter semantics beyond what the schema provides, such as explaining interactions between parameters or default behaviors. It mentions the data scope but not parameter-specific details, so it 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: retrieving 'EU international trade in goods data from Eurostat' with specific details like 'monthly and annual imports and exports by partner country' and coverage of 'intra-EU and extra-EU trade flows in millions of euros.' It distinguishes itself from sibling tools by focusing on Eurostat trade data, unlike other trade tools like 'trade__usitc' or economic tools like 'economic__comtrade,' which cover different sources or aspects.

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: for accessing Eurostat trade data with specific parameters like geo, partner, and indicator. However, it does not explicitly state when not to use it or name alternatives among sibling tools (e.g., 'economic__comtrade' for broader trade data or 'economic__wto-trade' for WTO 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.