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Czso Cz

Query ČSÚ open-data catalogs, inspect dataset metadata, summarize coverage, and retrieve observations as JSON-stat.

Install Czso Cz

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": {
    "czso-cz": {
      "command": "npx",
      "args": [
        "-y",
        "@pipeworx/mcp-czso-cz"
      ]
    }
  }
}

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

Czso Cz MCP server connects AI agents to the Czech Statistical Office (ČSÚ) open-data catalog and data layer. It supports dataset discovery, metadata inspection, size and coverage checks, and retrieval of complete datasets as JSON-stat 2.0. Reach for it when an agent needs Czech statistical data, especially when dataset structure and available dimensions must be checked before downloading values. The integration is unofficial and is available through Pipeworx Gateway or as a local stdio package.

Use cases

  • Find ČSÚ datasets by Czech title
  • Inspect dataset dimensions and update metadata
  • Estimate dataset size before retrieval
  • Retrieve complete Czech statistical datasets as JSON-stat

Key features

  • ČSÚ open-data catalog browsing
  • Dataset metadata and dimension inspection
  • Cell-count and time-range summaries
  • JSON-stat 2.0 observation retrieval
  • Hosted HTTP and local stdio modes

Tools4

What Czso Cz lets your agent do. Expand a tool for its parameters.

Verified live~633 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 4 tools this server returned when we ran it.

Makes network requests · 2Read-only tools · 4
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-czso-cz (npm)

Adoption & maintenance

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

GitHub stars2Stargazers on the source repository.
npm downloads617Package downloads in the last 30 days.
Last commit3d agoMost recent push to the default branch.
Tools exposed4Callable tools this server registers over MCP.
Directory activity1 viewsConfig copies, upvotes, and views on AllMCPs.

How Czso Cz works

What Czso Cz MCP server does

Czso Cz MCP server exposes the Czech Statistical Office (Český statistický úřad, ČSÚ) open-data catalog and its published datasets through MCP. It separates catalog exploration from data retrieval, which helps an agent identify the right dataset and understand its structure before requesting observations.

The catalog contains roughly 781 datasets. Dataset listings include the ČSÚ identifier (kod), version, Czech title, status, and available time and territory levels. Metadata for an individual dataset can include its description, keywords, indicators, dimension variants, selection rules, update frequency, and thematic classification.

The integration is an independent, unofficial connection and is not affiliated with, endorsed by, or published by ČSÚ.

How it works

Connect through the hosted Pipeworx MCP endpoint or run the package locally over standard input and output. The hosted pack endpoint also exposes shared Pipeworx meta-tools, while the standalone package exposes only the ČSÚ-related tools. Both approaches use the same underlying source and tool set.

A practical query sequence is:

  1. Search the catalog with list_datasets.
  2. Inspect a candidate using dataset_detail.
  3. Call data_summary to check the number of cells, time span, dimension sizes, and publication or modification timestamps.
  4. Use get_data only when the complete observation set is appropriate.

When a version is omitted, the data tools resolve it from the catalog. Catalog filtering uses a case-insensitive substring of the Czech dataset title. Because the ČSÚ API does not provide server-side title search, filtering is performed by the service, with pagination through limit and offset.

Setup and configuration

For a hosted connection, add the streamable HTTP endpoint to an MCP client:

config.json
{
  "mcpServers": {
    "czso-cz": {
      "url": "https://gateway.pipeworx.io/czso-cz/mcp"
    }
  }
}

The full Pipeworx gateway is also available at https://gateway.pipeworx.io/mcp. A pack endpoint lists the ČSÚ tools directly and includes shared gateway meta-tools; the full gateway lists tools from all available packs directly.

For local execution, use the npm package through a stdio configuration:

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

The local mode does not require a Pipeworx account or gateway round trip. No environment variables are required by the documented setup.

Tools and capabilities

  • list_datasets browses the ČSÚ catalog with title filtering and pagination.
  • dataset_detail returns detailed catalog metadata for a dataset identifier.
  • data_summary reports cell counts, time coverage, dimension value counts, and change or publication timestamps.
  • get_data returns the complete dataset as JSON-stat 2.0, including dimension identifiers, dimension metadata, cell sizes, and values.
  • The hosted gateway can also accept direct HTTP tool calls and natural-language requests through its shared routing tools.

Limitations and notes

get_data returns the full cross-product of a dataset rather than a narrowly selected slice. Large datasets may contain hundreds of thousands of cells and produce responses of 1 MB or more, so data_summary should be used first to estimate the response size.

The catalog and data responses use Czech field names and dataset titles. The catalog's title filter is substring-based because the upstream API has no server-side search. The hosted endpoint's exact shared-tool inventory can change; its initialize response is authoritative for the tools exposed in a particular connection.

The direct HTTP interface can be used without an MCP client for initial calls, while local stdio execution provides only the ČSÚ pack tools and does not include Pipeworx-wide routing.

Reviews

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Frequently Asked Questions about Czso Cz

Czso Cz is an MCP server that connects AI agents to the Czech Statistical Office open-data catalog and data layer. Its main tools list datasets, retrieve dataset metadata, summarize coverage and size, and fetch complete observations as JSON-stat 2.0, with versions resolved automatically when omitted.

Install it by configuring an MCP client to run `npx -y @pipeworx/mcp-czso-cz` with stdio transport. This local mode requires no Pipeworx account. You can instead connect to `https://gateway.pipeworx.io/czso-cz/mcp` over streamable HTTP.

Yes, Czso Cz works with Cursor through the hosted MCP endpoint or its local stdio configuration. Add the endpoint URL to Cursor's MCP server settings, or configure the command `npx` with the package argument `-y @pipeworx/mcp-czso-cz`.

Call `data_summary` before `get_data` to inspect the cell count, time range, and number of values in each dimension. `get_data` returns the complete cross-product and can produce hundreds of thousands of cells or responses larger than 1 MB.

Yes, the hosted gateway provides direct HTTP access to tools. Send a POST request to the corresponding `/v1/tools/...` endpoint with JSON input, or inspect a tool through its HTTP endpoint. The first calls do not require an account.

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