query_resourcePull rows from a tabular Finland 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": "12345678-abcd-ef01-2345-6789abcdef01"
+ },
+ {
+ "limit": 50,
+ "q": "Helsinki",
+ "resource_id": "87654321-dcba-fe10-5432-1098fedcba87"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare this is a safe read operation (readOnlyHint, destructiveHint false). The description adds a valuable behavioral constraint: only resources with DataStore enabled are queryable, which is not apparent from annotations or schema. This adds context beyond structured fields, warranting a score above baseline.
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 a single, front-loaded sentence followed by a brief note. Every word earns its place: the action, the resource type, the key parameters, and the critical caveat. No redundancy or fluff.
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 query tool with strong annotations and full schema coverage, the description covers the essential context: what it does, how to invoke it, and a non-obvious prerequisite. It does not detail the return format, but the phrase 'Pull rows' plus the overall simplicity make it sufficiently complete for an agent to use correctly.
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 coverage is 100%, with each parameter described in the schema. The description only repeats the optional params (q, limit, offset) and mentions resource_id's origin, but adds little beyond the schema. Baseline 3 is appropriate since the schema carries the parameter documentation burden.
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's function: pulling rows from a tabular Finland Open Data resource via resource_id. It uses a specific verb ('Pull rows') and identifies the resource type (CKAN DataStore), distinguishing it from sibling tools like search_datasets that search datasets rather than querying specific resource 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 implies usage context: it's for querying a specific resource after identifying it from a dataset's resources. It also includes a useful caveat (only DataStore-enabled resources are queryable). However, it does not explicitly name alternative tools or provide exclusionary guidance, though the context is clear.
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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