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Keboola Explorer

The Keboola MCP Server is a Model Context Protocol server for interacting with Keboola Connection, providing tools to access Keboola storage API data and supporting use on platforms such as Claude Desktop and Cursor AI.
Cloud platformDeveloper tools#Data storage#SQL query#Keboola tools#AI integration.Python
2.5 points
8.2K
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Overview

Installation

Content Details

Alternatives

What is the Keboola MCP Server?

The Keboola MCP Server is a middleware service that allows you to directly interact with the Keboola Connection platform through AI assistants (such as Claude or Cursor). It offers various functions such as querying data, managing buckets and tables, and running transformations without directly using the Keboola interface or API.

How to use the Keboola MCP Server?

You can connect to the MCP server by configuring an AI client (such as Claude Desktop or Cursor AI). After the server is running, you can directly interact with Keboola data using natural - language commands in the chat interface.

Use cases

It is suitable for data analysts, business intelligence users, and developers, especially those who want to quickly query Keboola data using natural language without writing complex SQL or using the full interface.

Main features

Storage tools
Browse and manage buckets and tables in Keboola storage, including viewing detailed information and updating descriptions.
SQL tools
Execute SQL queries directly in the Keboola workspace, supporting Snowflake and BigQuery.
Component tools
Manage and create component configurations, including SQL transformations.
Job tools
View and manage Keboola jobs, including starting new jobs.
Advantages
Simplify Keboola data access through a natural - language interface.
Support multiple AI clients (Claude, Cursor, etc.).
Provide comprehensive coverage of Keboola functions.
Support two backends, Snowflake and BigQuery.
Limitations
Require configuration of the Keboola API token and workspace.
BigQuery requires additional Google Cloud credentials.
Some advanced functions may require direct use of the Keboola interface.

How to use

Install the server
Install the keboola_mcp_server package via pip or use Smithery for automatic installation.
Configure the AI client
Edit the configuration file according to the AI client you are using (Claude or Cursor).
Set environment variables
Configure the Keboola storage API token and workspace information.
Start the server
Run the server and connect to the AI client.

Usage examples

Data exploration
Quickly browse the data structure and content in the Keboola project.
Data query
Query data in a specific table directly from the chat interface.
Transformation management
Create and manage data transformations.

Frequently Asked Questions

How to obtain the Keboola storage API token?
Why does BigQuery require additional configuration?
Which AI clients does the server support?
How to update the description of a bucket or table?

Related resources

GitHub repository
Project source code and issue tracking.
Keboola documentation
Official documentation for Keboola Connection.
MCP protocol specification
Technical specification for the Model Context Protocol.

Installation

Copy the following command to your Client for configuration
{
  "mcpServers": {
    "keboola": {
      "command": "uvx",
      "args": [
        "keboola_mcp_server",
        "--api-url",
        "https://connection.YOUR_REGION.keboola.com"
      ],
      "env": {
        "KBC_STORAGE_TOKEN": "your-keboola-storage-token",
        "KBC_WORKSPACE_SCHEMA": "your-workspace-schema"
      }
    }
  }
}

{
  "mcpServers": {
    "keboola": {
      "url": "http://localhost:8000/sse?storage_token=YOUR-KEBOOLA-STORAGE-TOKEN&workspace_schema=YOUR-WORKSPACE-SCHEMA"
    }
  }
}

{
  "mcpServers": {
    "keboola": {
      "command": "uvx",
      "args": [
        "keboola_mcp_server",
        "--transport",
        "stdio",
         "--api-url",
         "https://connection.YOUR_REGION.keboola.com"
      ],
      "env": {
        "KBC_STORAGE_TOKEN": "your-keboola-storage-token", 
        "KBC_WORKSPACE_SCHEMA": "your-workspace-schema"         
      }
    }
  }
}

{
  "mcpServers": {
    "keboola": {
      "command": "wsl.exe",
      "args": [
        "bash",
        "-c",
        "'source /wsl_path/to/keboola-mcp-server/.env",
        "&&",
        "/wsl_path/to/keboola-mcp-server/.venv/bin/python -m keboola_mcp_server.cli --transport stdio'"
      ]
    }
  }
}
Note: Your key is sensitive information, do not share it with anyone.

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