Katzilla MCP

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

The description adds valuable behavioral context beyond the annotations. While annotations indicate read-only, non-destructive, idempotent, and open-world traits, the description specifies the return format (Katzilla envelope with data, quality, citation), quality scoring (freshness/uptime/confidence), citation details (source URL, license, SHA-256 hash), and update frequency (monthly). This enriches the agent's understanding of output behavior and data provenance, though it could mention rate limits or authentication needs.

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 supporting details (data types, source, updates, return format) in a logical flow. Every sentence adds value: the second sentence elaborates on data scope, the third on source and frequency, and the fourth on output structure. It is efficiently structured with zero redundant information.

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 (EU transport statistics), rich annotations (read-only, idempotent, etc.), and the presence of an output schema, the description is complete enough. It covers purpose, data scope, source, update frequency, and detailed return behavior (including quality metrics and citation), which complements the structured fields. No significant gaps remain for agent understanding.

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 two parameters (geo and dataset), including enums and defaults. The description does not add any parameter-specific semantics beyond what the schema provides, such as explaining dataset choices or geo code formats. It meets the baseline for high schema coverage but does not compensate with extra insights.

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: it retrieves EU transport statistics from Eurostat, specifying the data types (passenger and freight transport by mode), geographic scope (EU/EEA countries), and indicators (modal split, volume, safety). It distinguishes itself from siblings like 'transport__bc-ferries' or 'transport__bts-stats' by focusing on Eurostat's EU-wide transport datasets, making the verb+resource+scope specific and well-differentiated.

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 implies usage context by mentioning the data source (Eurostat) and update frequency (monthly), but it does not explicitly state when to use this tool versus alternatives. For example, it does not compare with sibling tools like 'demographics__eurostat' or 'economic__eurostat-gdp', nor does it provide exclusions or prerequisites. The guidance is limited to the tool's scope without explicit alternatives.

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