Katzilla MCP

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

The description adds valuable behavioral context beyond the annotations. Annotations indicate read-only, non-destructive, idempotent, and open-world traits, but the description specifies the data source ('Department for Transport'), update frequency ('updates daily'), license ('Open Government Licence'), and output structure ('Katzilla envelope { data, quality, citation }') with details on quality scoring and citation contents. This enhances understanding of data freshness, reliability, and auditability 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 highly concise and well-structured, with three sentences that efficiently cover purpose, source, and output format without redundancy. Each sentence adds critical information: the first defines scope and data types, the second specifies source and updates, and the third details the return structure and its components. There is no wasted text, making it easy to parse and understand quickly.

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 as a data retrieval tool with annotations covering safety and idempotency, and an output schema (implied by 'Has output schema: true'), the description is complete. It covers the tool's purpose, data source, update frequency, license, and detailed output structure, including quality metrics and citation details. This provides sufficient context for an agent to use the tool effectively without needing to explain return values, as the output schema handles that.

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 the two parameters ('query' and 'limit') with descriptions and defaults. The description does not add any parameter-specific semantics beyond what the schema provides, such as examples of effective queries or guidance on limit selection. This meets the baseline score of 3, as the schema carries the full burden of parameter documentation.

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 with specific verbs ('returns') and resources ('UK transport statistics from the Department for Transport'), listing concrete data types like 'road traffic, vehicle registrations, bus usage, rail passenger numbers, and road safety data'. It clearly distinguishes itself from sibling tools by specifying its geographic and thematic scope (UK transport), unlike other transport tools like 'transport__bts-stats' (US) or 'transport__eurostat-transport' (EU).

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 accessing UK transport statistics from the Department for Transport, with daily updates. It implies usage for data retrieval rather than analysis or filtering, but does not explicitly state when not to use it or name specific alternatives among sibling tools, such as 'transport__eurostat-transport' for EU data or 'transport__bts-stats' for US data.

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