CKAN MCP Server

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It vaguely suggests exploration but fails to clarify key traits such as whether this is a read-only operation, if it requires authentication, what the output format might be, or any rate limits. The lack of detail leaves the agent with insufficient information about how the tool behaves beyond its name.

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 concise with a single phrase, but it is under-specified rather than efficiently informative. It lacks front-loaded clarity and wastes the opportunity to add meaningful context. While brief, it does not earn its place by providing actionable insights, making it mediocre in structure.

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 is low (0 parameters) but with no annotations or output schema, the description is incomplete. It does not explain what 'organizations and topic groups' refer to, how results are returned, or any behavioral nuances. For a tool in a data-focused context with siblings like 'list_datasets', more detail is needed to guide the agent effectively.

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?

The input schema has 0 parameters with 100% coverage, meaning no parameters are documented in the schema. The description does not add parameter details, which is acceptable since there are none to explain. However, it could hint at implicit parameters (e.g., filters), but its vagueness limits value. A baseline of 4 is appropriate for zero-parameter tools, as there is little to compensate for.

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 'Explore organizations and topic groups' is vague and tautological—it essentially restates the tool name 'get_data_categories' without specifying what 'explore' entails (e.g., list, retrieve, or browse). It lacks a clear verb-resource pairing and does not distinguish from siblings like 'list_datasets' or 'get_package', which are more specific. This leaves the purpose ambiguous for an AI agent.

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 no guidance on when to use this tool versus alternatives. It does not mention context, prerequisites, or exclusions, nor does it reference sibling tools like 'list_datasets' or 'get_package' for comparison. This absence of usage instructions makes it challenging for an agent to determine appropriate invocation scenarios.

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