Katzilla MCP

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

Annotations already cover read-only, non-destructive, idempotent, and open-world hints, so the description adds valuable context beyond that: it specifies the data source (data.police.uk), update frequency ('updates daily'), license (Open Government Licence), and details about the return format (Katzilla envelope with quality scores and citation). This enhances 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 front-loaded with the core purpose, followed by source details and return format explanation in a compact two-sentence structure. Every sentence adds value (e.g., data source, update frequency, return envelope details) with zero waste, making it highly efficient and well-organized.

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 (querying crime data with parameters), rich annotations (covering safety and behavior), and the presence of an output schema (implied by return format details), the description is complete. It adds necessary context like source, license, update frequency, and return structure, compensating well for any gaps and ensuring the agent has sufficient information.

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%, so the schema already documents all parameters (lat, lng, date) with defaults and descriptions. The description adds no additional parameter semantics beyond implying location and date filtering, which is already covered. Baseline 3 is appropriate as the schema handles the heavy lifting.

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 with specific verbs ('Query street-level crime data') and resources ('UK Police API by location and date'), distinguishing it from siblings by specifying the exact data source and scope. It explicitly mentions the source (data.police.uk) and the type of data returned, making it distinct from other tools in the list.

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 usage ('by location and date') and mentions the data source and update frequency ('updates daily'), which helps in understanding when to use it. However, it does not explicitly state when not to use this tool or name specific alternatives among siblings, though the context implies it's for UK crime data queries.

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