Katzilla MCP

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

The description adds valuable behavioral context beyond the annotations: it specifies the data source ('National Park Service (Public Domain)'), update frequency ('updates daily'), and details about the return format ('Katzilla envelope { data, quality, citation }') including quality metrics and citation information. While annotations cover read-only, non-destructive, idempotent, and open-world hints, the description enriches this with practical implementation details without contradicting the 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 efficiently structured in two sentences: the first states the tool's purpose and source, and the second explains the return format and its components. Every sentence provides essential information without redundancy, making it front-loaded and easy to parse for an 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 simplicity (2 parameters, 100% schema coverage, annotations covering key behavioral hints, and an output schema implied by the return format description), the description is complete. It covers purpose, data source, update frequency, and return structure, which suffices for an agent to understand and invoke the tool effectively without needing additional explanations.

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' for search and 'limit' for max results). The description does not add any parameter-specific semantics beyond what the schema provides, such as query syntax examples or limit constraints, so it meets the baseline expectation without extra value.

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 as 'Search US National Parks using the National Park Service API', which includes a specific verb ('Search'), resource ('US National Parks'), and data source ('National Park Service API'). It clearly distinguishes itself from sibling tools like 'geo__census-geocoder' or 'geo__nominatim' by focusing exclusively on national parks data rather than general geocoding or mapping services.

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 implies usage context by mentioning the data source ('National Park Service API') and update frequency ('updates daily'), but does not explicitly state when to use this tool versus alternatives. It lacks guidance on prerequisites, limitations, or comparisons with other geography-related tools in the sibling list, leaving the agent to infer appropriate usage scenarios.

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