Open Data MCP

query

Read-only

Run read-only SQL against a single tabular resource (DataStore, CSV, or JSON) to answer questions about Ukrainian open data; rows returned are capped.

Instructions

Run read-only SQL over one tabular resource (DataStore, CSV or JSON). The SQL always covers the whole resource or the call fails; only the returned rows are capped.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "dataset_id": "<dataset_id>",
      "resource_id": "<resource_id>",
      "sql": "<sql>"
    },
    "name": "query"
  }
}

Input Schema

Name	Required	Description
`sql`	Yes	One read-only SELECT over the table `data`; CTEs allowed.
`limit`	No	Max rows returned (default 20, hard max 100).
`dataset_id`	Yes	Dataset id or name.
`resource_id`	Yes	Resource id inside that dataset.

Output Schema

Name	Required	Description
`ok`	Yes
`note`	No
`rows`	No
`error`	No
`source`	No	Provenance; always present when ok is true.
`columns`	No
`citation`	No	One-line source note to include in the answer.
`row_limit`	No	Server-enforced cap on returned rows.
`truncated`	No
`scope_note`	No
`returned_rows`	No
`rows_in_resource`	No	Rows in the whole resource the SQL ran over.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 18, 2026

TDQS

A3.8/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=true, so safety is covered. The description adds genuinely new behavioral context beyond that: the SQL must cover the whole resource or the call fails, and only returned rows are capped — a meaningful constraint on how queries may be written.

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?

Two sentences, zero filler. The core capability is front-loaded and the constraint on SQL scope follows immediately, so nothing needs to be read twice.

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

Completeness4/5

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

An output schema exists, so return values need no explanation, and all four parameters are fully covered by the schema. The description supplies the key semantic constraints; the only real gap is routing guidance versus the sibling tools.

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 description coverage is 100%, so sql, limit, dataset_id and resource_id are all documented in the schema (including the SELECT-only rule and the default 20 / hard max 100 limit). The description only reinforces the row-cap concept via 'only the returned rows are capped', adding no syntax or format detail beyond the schema.

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

Purpose4/5

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

The description gives a specific verb and resource: running read-only SQL over one tabular resource (DataStore, CSV or JSON). That clearly separates it from a schema-inspection or dataset-search tool, though it never names the siblings search_datasets or get_schema to sharpen the boundary.

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?

Usage is implied — an agent infers this is the tool for retrieving rows from a resource — but there is no explicit when-to-use statement, no prerequisite (e.g. needing an id from search_datasets/get_schema), and no exclusion rules relative to the siblings.

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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