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 (Department for Education), licensing (Open Government Licence), update frequency (annual), and return format (Katzilla envelope with quality scores and citation details). While annotations already indicate read-only, non-destructive, idempotent, and open-world behavior, the description provides important operational details about data provenance and structure.

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 three sentences: first establishes purpose and scope, second specifies source and licensing, third details return format. Every sentence adds essential information with zero wasted words, and key information is front-loaded.

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, comprehensive annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), complete schema coverage, and existence of an output schema, the description provides excellent contextual completeness. It covers data source, licensing, update frequency, and return format details that aren't captured in 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 already fully documents both parameters. The description doesn't add specific parameter semantics beyond what's in the schema, though it implies the 'query' parameter searches education datasets. 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 clearly states the tool's purpose: retrieving UK education statistics from the Department for Education, specifying the data domains (school performance, pupil characteristics, workforce data, educational outcomes) and geographic scope (England). It distinguishes itself from sibling tools like 'education__college_scorecard' by focusing on UK government data rather than US college data.

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 about when to use this tool: for UK education statistics with annual updates from the Department for Education. It doesn't explicitly state when not to use it or name specific alternatives, but the geographic and topical focus gives strong implicit guidance compared to other education tools in the sibling list.

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