query_resourcePull rows from a tabular Slovenia Open Data resource (CKAN DataStore) by resource_id (from a dataset's resources). Optional full-text q, limit, offset. Note: only resources with the DataStore enabled are queryable.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | Optional full-text filter over the rows. | |
| limit | No | Max rows (default 100). | |
| offset | No | Pagination offset. | |
| resource_id | Yes | A resource_id from dataset/search_datasets. |
Changes observed during successful MCP inspections.
Input schema / examplesAdded value: +[
+ {
+ "resource_id": "d8f5c3e2-1a9b-4f2e-8c7d-5e3f9a2b1c0d"
+ },
+ {
+ "limit": 50,
+ "q": "Ljubljana",
+ "resource_id": "d8f5c3e2-1a9b-4f2e-8c7d-5e3f9a2b1c0d"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering the safety profile. The description adds the useful constraint that only DataStore-enabled resources are queryable, which is valuable context. However, it does not disclose behavior such as error handling or response format beyond 'pull rows'.
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 two sentences, with the main action front-loaded and a brief note on the DataStore requirement. Every word earns its place, making it concise and well-structured.
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 simple read-only query tool with thorough parameter documentation and safety annotations, the description is fairly complete. It explains the resource source, the DataStore limitation, and indicates the output is rows. The lack of an output schema is not a gap since the description already states it returns rows.
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%, and the schema already explains each parameter fully. The description only mentions 'full-text q, limit, offset' without adding any new meaning beyond what the schema provides, so it meets the baseline but does not exceed it.
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 pulls rows from a tabular CKAN DataStore resource by resource_id, specifying the data source (Slovenia Open Data) and the exact operation. It distinguishes itself from siblings like search_datasets or search_within by focusing on querying a specific resource's data.
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: you need a resource_id from a dataset's resources, and it notes the prerequisite that the DataStore must be enabled. It implies a workflow with search_datasets but does not explicitly name alternatives or when-not-to-use, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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