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 but provides minimal information. It mentions 'deep data structure analysis' but doesn't explain what this analysis returns, whether it's a read-only operation, performance characteristics, or any side effects. For a tool with 12 parameters and no annotation coverage, this is inadequate.

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 extremely concise at just 7 words. It's front-loaded with the core purpose and wastes no words. Every word earns its place in conveying the tool's function, though it could benefit from additional context.

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?

For a complex tool with 12 parameters, no annotations, no output schema, and many sibling alternatives, the description is incomplete. It doesn't explain what the analysis returns, how it differs from other dataset tools, or provide behavioral context. The 100% schema coverage helps with parameters, but the overall context for tool selection and understanding is insufficient.

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 schema description coverage is 100%, so all parameters are documented in the schema. The description doesn't add any parameter-specific information beyond what's already in the schema descriptions. It mentions 'field definitions' which relates to the analysis output but doesn't clarify parameter usage. Baseline 3 is appropriate when schema does 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 states 'Deep data structure analysis with field definitions', which provides a general purpose (analyzing dataset structure) but is somewhat vague. It doesn't specify what 'deep analysis' entails or how this differs from sibling tools like 'get_dataset_insights' or 'get_package'. The verb 'analyze' is clear but lacks specificity about the analysis scope.

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. With 13 sibling tools including 'get_dataset_insights', 'get_package', and 'get_first_datastore_resource_records', there's no indication of when this analysis tool is preferred over other dataset inspection tools. No prerequisites or exclusions are mentioned.

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