Katzilla MCP

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

Annotations indicate read-only, non-destructive, idempotent, and open-world behavior, which the description does not contradict. The description adds valuable context beyond annotations: it specifies the data source (Environment and Climate Change Canada), update frequency ('updates daily'), and return format details (Katzilla envelope with quality scores and citation info), enhancing transparency about data freshness and auditability.

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 scope, source, and return format details in a logical flow. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.

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 (2 parameters, 100% schema coverage, annotations, and an output schema), the description is complete. It covers purpose, scope, source, update frequency, and return structure, compensating well for any gaps. With annotations and output schema handling safety and return values, no critical information is missing.

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 clear parameter descriptions in the schema. The description does not add any additional meaning or examples for the parameters beyond what the schema provides, such as explaining typical use cases for 'province' or 'limit'. However, it meets the baseline of 3 since the schema adequately documents the parameters.

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: retrieving 'active weather alerts and warnings from Environment and Climate Change Canada via the MSC GeoMet API.' It specifies the scope ('Covers all provinces and territories') and the types of alerts ('severe weather warnings, watches, advisories, and special statements'), clearly distinguishing it from other environment tools like climate data or air quality tools.

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 real-time severe weather alerts in Canada, with daily updates. It implies usage for safety or monitoring purposes but does not explicitly state when not to use it or name specific alternatives among the sibling tools, such as 'hazards__nws-alerts' for U.S. alerts or other environment tools.

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