mcpField
D
Datacube Sk

Datacube Sk

Remote
pipeworx.ioLegal & Government0Updated 4d ago

About Datacube Sk

Datacube Sk is an MCP server for exploring and retrieving statistical data from DATAcube, the Statistical Office of the Slovak Republic. Its tools expose cube discovery, dimension values, and JSON-stat data retrieval for users working with Slovak statistics.

Key features

  1. 01Search and browse statistical cubes
  2. 02List dimensions and valid value codes
  3. 03Retrieve data in JSON-stat format
  4. 04Run locally over stdio or use Pipeworx gateway

Use cases

  1. 01Find a DATAcube series by label
  2. 02Check dimension codes before selecting data
  3. 03Retrieve selected Slovak statistical data

MCP support

Exposes DATAcube dataset discovery, dimension lookup, and data retrieval tools to MCP clients. It can run locally over stdio or be accessed through the Pipeworx gateway.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://gateway.pipeworx.io/datacube-sk/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

Tools

Details

From the project README

mcp-datacube-sk

DATAcube — Statistical Office of the Slovak Republic (data.statistics.sk).

Part of Pipeworx — an MCP gateway connecting AI agents to 1878+ live data sources. This is an independent, unofficial integration — not affiliated with, endorsed by, or published by the upstream provider.

Tools

ToolDescription
list_datasetsBrowse / search the DATAcube collection of Slovak statistical cubes. Filter by case-insensitive substring of the English label. Returns each matching cube's code (use it as cube in the other tools), label, last update date, and its ordered list of dimension codes.
dataset_dimensionsList the dimensions of one cube, in path order, with each dimension's valid value codes and labels. Selections in get_data are positional and must follow this dimension order. Get the cube code from list_datasets.
get_dataPull data for a cube as JSON-stat. Provide one selection per dimension, in the dimension order returned by dataset_dimensions. Each selection is either a single value code, a comma-separated list of codes (e.g. "SEX_M,SEX_F"), or the literal "all" for every value of that dimension. The number of selections must equal the cube's dimension count. Output is JSON-stat: value is the flat data array, dimension/size/id describe its shape.

Quick Start

Add to your MCP client (Claude Desktop, Cursor, Windsurf, etc.):

{
  "mcpServers": {
    "datacube-sk": {
      "url": "https://gateway.pipeworx.io/datacube-sk/mcp"
    }
  }
}
What this endpoint actually serves

tools/list at https://gateway.pipeworx.io/datacube-sk/mcp returns the tools in the table above plus the shared Pipeworx meta-tools — ask_pipeworx, discover_tools, search_within, remember/recall and the rest of the gateway-wide set. So the tool count you see is larger than this table: a single-pack endpoint currently lists roughly 30 shared tools alongside the pack's own. The connection's initialize response states its exact scope, and is the authoritative answer for a given day.

This is deliberate, not multiplexing by accident. The meta-tools are what let a scoped connection answer a question this pack does not cover — via ask_pipeworx, which routes across the whole catalog — without you adding a second MCP server. There is currently no way to mount a pack endpoint without them; if the extra schemas cost you more context than the routing is worth, connect to the full gateway once rather than to several pack endpoints.

Or connect to the full Pipeworx gateway to get every pack's tools listed directly, instead of just this one's:

{
  "mcpServers": {
    "pipeworx": {
      "url": "https://gateway.pipeworx.io/mcp"
    }
  }
}

Both URLs reach the same gateway and the same 1878+ data sources. The only difference is which pack's tools are listed directly; ask_pipeworx reaches all of them from either one.

No MCP client? Call it over HTTP

curl -X POST https://gateway.pipeworx.io/v1/tools/datacube_sk_list_datasets \
  -H 'Content-Type: application/json' \
  -d '{"query":"population"}'

No account needed for the first calls. Inspect any tool: GET https://gateway.pipeworx.io/v1/tools/datacube_sk_list_datasets. Find one: POST https://gateway.pipeworx.io/v1/tools/search_packs with {"query":"..."}.

Standalone (no gateway account)

This package also runs as a local stdio MCP server — no Pipeworx account, no gateway round-trip:

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

Or run it directly to confirm it starts:

npx -y @pipeworx/mcp-datacube-sk

It speaks MCP over stdin/stdout and answers initialize/tools/list/tools/call for only this pack's tools — none of the shared meta-tools the gateway connection above adds. Same source, same tools, no ask_pipeworx routing.

Using with ask_pipeworx

Instead of calling tools directly, you can ask questions in plain English — this works on the pack endpoint above as well as on the full gateway:

ask_pipeworx({ question: "your question about Datacube Sk data" })

The gateway picks the right tool and fills the arguments automatically.

More

License

MIT

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.