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Datacube Sk

Query Slovak Statistical Office DATAcube datasets, inspect dimensions, and retrieve selected data as JSON-stat.

Install Datacube Sk

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "datacube-sk": {
      "command": "npx",
      "args": [
        "-y",
        "@pipeworx/mcp-datacube-sk"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Install not yet sandbox-tested— the server itself is confirmed live (we called its tools/list endpoint). The stdio install command is a separate, ongoing check.

Overview

Datacube Sk connects MCP clients to DATAcube, the Statistical Office of the Slovak Republic's statistical cube collection at data.statistics.sk. It lets agents search datasets, inspect dimension codes and values, then retrieve selected observations as JSON-stat. Use it when you need structured Slovak statistical data and can express the query through DATAcube's cube and dimension selections. It is available through the Pipeworx gateway or as a standalone local stdio package.

Use cases

  • Search Slovak statistical cubes by English label
  • Inspect valid dimension and value codes before querying
  • Retrieve selected cube observations as JSON-stat
  • Compare multiple coded values within a dimension

Key features

  • DATAcube dataset search
  • Dimension and value-code discovery
  • Positional multi-dimension queries
  • JSON-stat responses
  • Hosted HTTP and local stdio access

Tools3

What Datacube Sk lets your agent do. Expand a tool for its parameters.

Verified live~519 tokens of context

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 3d ago

Recent health check succeeded.

7/7 checks healthy over the last 39d

What it can do

From the 3 tools this server returned when we ran it.

Makes network requests · 1Read-only tools · 3
Network at startup3 connections
Files written at startupNone
Install scriptsNone
npm provenanceSigned build
Package age139 days
Maintainers1

Observed 2026-10-19 in an isolated sandbox. A description of reach, not a security audit.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 4d ago via OSV.dev · @pipeworx/mcp-datacube-sk (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

npm downloads675Package downloads in the last 30 days.
Last commit3d agoMost recent push to the default branch.
Tools exposed3Callable tools this server registers over MCP.
Directory activity1 viewsConfig copies, upvotes, and views on AllMCPs.

How Datacube Sk works

What Datacube Sk does

Datacube Sk exposes Slovak statistical cubes from DATAcube, the Statistical Office of the Slovak Republic's data service. Its three pack-specific tools support the full path from finding a cube to retrieving observations: search the collection, inspect the cube's dimensions, and submit a positional data query.

The integration is unofficial and is not affiliated with, endorsed by, or published by the upstream statistical office. Results are returned in machine-readable formats suited to agent workflows, including JSON-stat for data responses.

How it works

A typical query begins with list_datasets. Supply an optional case-insensitive substring from an English dataset label to narrow the collection. Each match includes a cube code, label, last update date, and ordered dimension codes.

Pass the selected cube code to dataset_dimensions. This returns the dimensions in path order, along with the valid value codes and human-readable labels for each one. That order matters: get_data expects one selection for every dimension, using exactly the sequence returned by this tool.

For each dimension, get_data accepts one value code, several comma-separated codes, or all. The response uses JSON-stat: value contains the flattened data array, while dimension, size, and id describe how the values are arranged.

Setup and configuration

The hosted MCP endpoint is:

text
https://gateway.pipeworx.io/datacube-sk/mcp

An MCP client can register that URL under a datacube-sk server entry. The endpoint also exposes shared Pipeworx meta-tools in addition to the pack-specific tools, so its tools/list result is larger than the three tools described here. The gateway's ask_pipeworx tool can route natural-language requests across the broader catalog, including this data source.

For a connection containing only the Datacube Sk tools, run the npm package locally over standard input and output:

config.json
{
  "mcpServers": {
    "datacube-sk": {
      "command": "npx",
      "args": ["-y", "@pipeworx/mcp-datacube-sk"]
    }
  }
}

The package can also be started directly with npx -y @pipeworx/mcp-datacube-sk. No Pipeworx account is required for the initial hosted HTTP calls, and the standalone mode does not use a gateway account or gateway round-trip.

Tools and capabilities

  • list_datasets searches DATAcube by a case-insensitive substring of the English label and returns identifiers, labels, update dates, and dimension-code order.
  • dataset_dimensions describes a cube's dimensions and their allowed codes and labels.
  • get_data retrieves one cube selection as JSON-stat, supporting individual values, comma-separated value lists, or all values for each dimension.

The hosted service can also be called through the Pipeworx HTTP tools API. For example, dataset search uses a POST request to https://gateway.pipeworx.io/v1/tools/datacube_sk_list_datasets with a JSON query field.

Limitations and notes

get_data requires the number of selections to equal the cube's dimension count. Selections are positional rather than named, so skipping the dimension-discovery step can produce incorrectly formed requests or misaligned results. Cube and value codes should be taken from the discovery tools instead of inferred from labels.

The standalone process exposes only this pack's tools. The hosted pack endpoint adds shared gateway meta-tools, while the full Pipeworx gateway lists tools from all available packs directly. Both hosted URLs use the same gateway and can reach the broader catalog through routing.

Reviews

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Frequently Asked Questions about Datacube Sk

Datacube Sk is an MCP server that connects AI agents to DATAcube, the Statistical Office of the Slovak Republic's statistical cube collection. Its main tools search datasets, list cube dimensions and valid values, and retrieve selected observations as JSON-stat using positional dimension selections.

Install it by registering the hosted MCP URL or running the standalone npm package with npx. Use `https://gateway.pipeworx.io/datacube-sk/mcp` for the gateway, or configure `npx -y @pipeworx/mcp-datacube-sk` as a local stdio server.

Yes, Datacube Sk works with Cursor through its hosted MCP URL. Add `https://gateway.pipeworx.io/datacube-sk/mcp` as an MCP server; the same endpoint also includes shared Pipeworx meta-tools.

First call `list_datasets` to find a cube code, then call `dataset_dimensions` to obtain the dimension order and valid value codes. Pass one selection per dimension to `get_data`, using a single code, comma-separated codes, or `all`.

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