search_datasetsSearch Finland Open Data (Finland) for datasets by keyword. Returns each dataset's id/name, title, organization, and its resources (each with a resource_id for query_resource).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max datasets (1-100, default 20). | |
| query | No | Keyword(s); blank/"*" lists all. | |
| offset | No | Pagination offset. |
Changes observed during successful MCP inspections.
Input schema / examplesAdded value: +[
+ {
+ "query": "population statistics"
+ },
+ {
+ "limit": 10,
+ "offset": 0,
+ "query": "education"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds behavioral context beyond these by specifying the return structure (id/name, title, organization, resources with resource_ids) and the intended downstream use with query_resource. This exceeds the baseline expectation.
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 concise sentences that front-load the action and resource. Every part adds value: the search function, the data source, the return fields, and the linkage to query_resource. No redundant wording.
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 search tool with optional parameters and no output schema, the description effectively communicates return structure and workflow. Combined with the rich annotations and schema, it provides complete guidance for an agent to select and invoke the tool 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?
Input schema description coverage is 100%, with clear descriptions for query, limit, and offset. The description adds no parameter-specific semantics beyond what the schema already provides. Baseline 3 is appropriate.
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 uses a specific verb ('Search') with a clear resource ('Finland Open Data datasets') and distinguishes itself from siblings by explaining that it returns resource IDs for use with query_resource. It clearly states the scope (by keyword) and output fields, making its purpose unambiguous.
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 that this tool is for discovering datasets and obtaining resource_ids to be used with query_resource, implying a workflow. It doesn't explicitly exclude alternatives, but the linkage to query_resource effectively guides usage. A 5 would require explicit 'when not to use' or direct alternative naming.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.