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Data Lv

by io.github.pipeworx-io
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Search Datasets

search_datasets
Read-onlyIdempotent

Search Latvia Open Data (Latvia) for datasets by keyword. Returns each dataset's id/name, title, organization, and its resources (each with a resource_id for query_resource).

Example tool call

This call uses example arguments supplied in the tool’s input schema.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "population"
    },
    "name": "search_datasets"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMax datasets (1-100, default 20).
queryNoKeyword(s); blank/"*" lists all.
offsetNoPagination offset.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "query": "population"
+  },
+  {
+    "limit": 10,
+    "offset": 0,
+    "query": "economy"
+  }
+]
  2. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, openWorld, and non-destructive. The description adds valuable behavioral context by specifying the exact return structure (dataset id/name, title, organization, resources with resource_id), which helps the agent understand what the tool provides.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences: the first defines purpose, the second details return fields. Every sentence adds value with no redundancy, and key information is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 3 parameters with full schema coverage, no output schema, and comprehensive annotations, the description provides complete context: it names the data source (Latvia Open Data), specifies what is returned, and links to a sibling tool (query_resource). No gaps remain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, with clear descriptions for all parameters (limit, query, offset). The description does not add any additional parameter semantics beyond what is already in the schema, so baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'search' and the resource 'Latvia Open Data datasets.' It lists specific return fields (id/name, title, organization, resources with resource_id), which distinguishes it from sibling tools like query_resource or search_within.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for searching datasets and mentions query_resource for next steps, but it does not explicitly state when to use this tool over alternatives, nor does it provide exclusion criteria.

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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