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 with copyright/license policy), update frequency (monthly), and detailed return structure (Katzilla envelope with quality scores and citation details including SHA-256 hash). While annotations already indicate read-only, non-destructive, idempotent, and open-world characteristics, the description enriches this with practical implementation details that help the agent understand data provenance and quality assessment.

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 establishes purpose and scope, the second covers source, updates, and return format. Every element serves a clear purpose with zero redundant information. It's appropriately front-loaded with the core functionality before detailing implementation specifics.

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 (3 parameters with enums), comprehensive annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), 100% schema coverage, and existence of an output schema, the description provides excellent contextual completeness. It covers data source, update frequency, return structure with quality metrics, and licensing information - all valuable additions beyond what structured fields provide.

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 and comprehensive enum documentation for geo, nrg_bal, and siec parameters, the schema already provides complete parameter information. The description doesn't add any parameter-specific semantics beyond what's in the schema, but it does provide overall context about the data domain that helps interpret parameter choices. This meets the baseline expectation when schema coverage is complete.

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 explicitly states the tool's purpose: retrieving 'Energy supply, transformation, and consumption data from Eurostat' with specific coverage details (EU/EEA countries, annual data on primary energy production, imports, final consumption, energy dependency). It clearly distinguishes itself from sibling tools by specifying its domain (energy) and data source (Eurostat), unlike siblings like agriculture__usda-nass or economic__eurostat-gdp.

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 the tool: for Eurostat energy data covering EU/EEA countries with monthly updates. It mentions the return format (Katzilla envelope) which helps understand output structure. However, it doesn't explicitly state when NOT to use it or name specific alternatives among siblings (e.g., economic__eurostat-gdp for economic data).

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