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 (dati.gov.it), license (Italian Open Data Licence 2.0), update frequency (daily), and return format (Katzilla envelope with quality scores and citation details). While annotations cover read-only, non-destructive, idempotent, and open-world characteristics, the description provides practical implementation details that help the agent understand what to expect.

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 with zero wasted words. It front-loads the core purpose, then provides essential context about source, license, updates, and return format. Every sentence adds value, and the information density is high without being verbose.

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 (search with quality scoring), rich annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), and existence of an output schema, the description provides excellent contextual completeness. It covers source, license, update frequency, and return structure, making it fully sufficient for an agent to understand when and how to use this tool effectively.

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 already fully documents both parameters (query and limit). The description doesn't add specific parameter semantics beyond what's in the schema, such as query syntax examples or limit implications. The baseline score of 3 reflects adequate parameter documentation through the schema alone.

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 ('Search'), target resource ('Italian government open data portal'), and scope ('datasets from Italian national and regional agencies covering economy, environment, society, transport, and culture'). It distinguishes itself from siblings by specifying the Italian focus and dati.gov.it source, unlike other government data tools like data-australia or data-canada.

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 searching Italian government open data across multiple domains. It implicitly distinguishes from siblings by specifying the Italian focus, but does not explicitly state when not to use it or name specific alternative tools for other regions or data types.

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