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 (Open Data)'), update frequency ('updates monthly'), and detailed return structure ('Katzilla envelope { data, quality, citation }') with quality metrics and citation details. 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 updates; the second details the return format. Every sentence adds critical information (e.g., return envelope structure, quality scores, citation details) with zero waste, making it front-loaded and highly 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 (statistical querying), rich annotations (read-only, idempotent, etc.), and the presence of an output schema, the description is complete. It covers purpose, source, update frequency, and return format in detail, compensating for any gaps. The output schema likely defines the return structure, so the description's elaboration on the 'Katzilla envelope' provides helpful context without redundancy.

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?

Schema description coverage is 100%, with both parameters ('geo' and 'year') fully documented in the schema. The description does not add any parameter-specific details beyond what the schema provides (e.g., it doesn't explain 'geo' codes or 'year' formatting further). Thus, it meets the baseline of 3 where the schema handles parameter semantics adequately.

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 specific action ('Query immigration statistics'), resource ('from Eurostat'), and scope ('data on immigration by age and sex for EU member states'). It distinguishes this tool from siblings like 'demographics__eurostat' by specifying the migration domain, making the purpose unambiguous 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 (Open Data)') and update frequency ('updates monthly'), but it does not explicitly state when to use this tool versus alternatives (e.g., other Eurostat tools like 'economic__eurostat-gdp' or general demographic tools). No exclusions or prerequisites are provided, leaving usage guidance at an implied level.

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