What is Keboola MCP Server?

Keboola MCP Server is an open-source bridge that connects your Keboola project with modern AI tools like cursor, Claude, and others. It enables the integration of Keboola's storage access, SQL transformations, and job triggers into callable tools for AI assistants.

How to use Keboola MCP Server?

1. Install Dependencies: Ensure Python 3.10+ is installed and you have access to a Keboola project with admin rights.
2. Configuration: Set up environment variables like KBC_STORAGE_TOKEN, KBC_WORKSPACE_SCHEMA, and your Keboola region.
3. Running Methods: Integrate with AI tools via MCP clients, use in local development, manually via CLI, or through Docker.
4. Query Data: Once configured, query data using natural language or predefined tool functions.

Key Features

• Storage: Query tables directly and manage table or bucket descriptions
• Components: Create, list, and inspect extractors, writers, data apps, and transformation configurations
• SQL: Create SQL transformations with natural language
• Jobs: Run components and transformations, and retrieve job execution details
• Metadata: Search, read, and update project documentation and object metadata using natural language

Use Cases

1. Building robust data workflows and integrations on the Keboola platform.
2. Analyzing and transforming data using SQL transformations via natural language commands.
3. Automating data extraction and processing jobs based on predefined configurations.
4. Enhancing AI assistants with the ability to query and manage Keboola data programmatically.

FAQ

• Do I need to run the server manually? It depends on your setup. For Claude/Cursor, no manual setup is needed. For testing or development, you can run it manually or use Docker.
• Which MCP clients are supported? Supported clients include Claude, Cursor, Windsurf, Zed, Replit, Codeium, and Sourcegraph.
• How do I troubleshoot common issues? Common issues include authentication errors, workspace issues, and connection timeouts. Verify the environment variables and check network connectivity.

Keboola MCP Server

Connect your AI agents, MCP clients (Cursor, Claude, Windsurf, VS Code ...) and other AI assistants to Keboola. Expose data, transformations, SQL queries, and job triggers—no glue code required. Deliver the right data to agents when and where they need it.

Overview

Keboola MCP Server is an open-source bridge between your Keboola project and modern AI tools. It turns Keboola features—like storage access, SQL transformations, and job triggers—into callable tools for Claude, Cursor, CrewAI, LangChain, Amazon Q, and more.

Features

• Storage: Query tables directly and manage table or bucket descriptions
• Components: Create, List and inspect extractors, writers, data apps, and transformation configurations
• SQL: Create SQL transformations with natural language
• Jobs: Run components and transformations, and retrieve job execution details
• Metadata: Search, read, and update project documentation and object metadata using natural language

Preparations

Make sure you have:

• [ ] Python 3.10+ installed
• [ ] Access to a Keboola project with admin rights
• [ ] Your preferred MCP client (Claude, Cursor, etc.)

Note: Make sure you have uv installed. The MCP client will use it to automatically download and run the Keboola MCP Server. Installing uv:

macOS/Linux:

#if homebrew is not installed on your machine use:
# /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

# Install using Homebrew
brew install uv

Windows:

# Using the installer script
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

# Or using pip
pip install uv

# Or using winget
winget install --id=astral-sh.uv -e

For more installation options, see the official uv documentation.

Before setting up the MCP server, you need three key pieces of information:

KBC_STORAGE_TOKEN

This is your authentication token for Keboola:

For instructions on how to create and manage Storage API tokens, refer to the official Keboola documentation.

Note: If you want the MCP server to have limited access, use custom storage token, if you want the MCP to access everything in your project, use the master token.

KBC_WORKSPACE_SCHEMA

This identifies your workspace in Keboola and is used for SQL queries. However, this is only required if you're using a custom storage token instead of the Master Token:

• If using Master Token: The workspace is created automatically behind the scenes
• If using custom storage token: Follow this Keboola guide to get your KBC_WORKSPACE_SCHEMA

Note: When creating a workspace manually, check Grant read-only access to all Project data option

Note: KBC_WORKSPACE_SCHEMA is called Dataset Name in BigQuery workspaces, you simply click connect and copy the Dataset Name

Keboola Region

Your Keboola API URL depends on your deployment region. You can determine your region by looking at the URL in your browser when logged into your Keboola project:

Region	API URL
AWS North America	https://connection.keboola.com
AWS Europe	https://connection.eu-central-1.keboola.com
Google Cloud EU	https://connection.europe-west3.gcp.keboola.com
Google Cloud US	https://connection.us-east4.gcp.keboola.com
Azure EU	https://connection.north-europe.azure.keboola.com

Running Keboola MCP Server

There are four ways to use the Keboola MCP Server, depending on your needs:

Option A: Integrated Mode (Recommended)

In this mode, Claude or Cursor automatically starts the MCP server for you. You do not need to run any commands in your terminal.

1. Configure your MCP client (Claude/Cursor) with the appropriate settings
2. The client will automatically launch the MCP server when needed

Claude Desktop Configuration

1. Go to Claude (top left corner of your screen) -> Settings → Developer → Edit Config (if you don't see the claude_desktop_config.json, create it)
2. Add the following configuration:
3. Restart Claude desktop for changes to take effect

{
  "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"
      }
    }
  }
}

Config file locations:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

Cursor Configuration

1. Go to Settings → MCP
2. Click "+ Add new global MCP Server"
3. Configure with these settings:

{
  "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"
      }
    }
  }
}

Cursor Configuration for Windows WSL

When running the MCP server from Windows Subsystem for Linux with Cursor AI, use this configuration:

{
  "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'"
      ]
    }
  }
}

Where /wsl_path/to/keboola-mcp-server/.env file contains environment variables:

export KBC_STORAGE_TOKEN="your_keboola_storage_token"
export KBC_WORKSPACE_SCHEMA="your_workspace_schema"

Option B: Local Development Mode

For developers working on the MCP server code itself:

1. Clone the repository and set up a local environment
2. Configure Claude/Cursor to use your local Python path:

{
  "mcpServers": {
    "keboola": {
      "command": "/absolute/path/to/.venv/bin/python",
      "args": [
        "-m", "keboola_mcp_server.cli",
        "--transport", "stdio",
        "--api-url", "https://connection.YOUR_REGION.keboola.com"
      ],
      "env": {
        "KBC_STORAGE_TOKEN": "your_keboola_storage_token",
        "KBC_WORKSPACE_SCHEMA": "your_workspace_schema",

      }
    }
  }
}

Option C: Manual CLI Mode (For Testing Only)

You can run the server manually in a terminal for testing or debugging:

# Set environment variables
export KBC_STORAGE_TOKEN=your_keboola_storage_token
export KBC_WORKSPACE_SCHEMA=your_workspace_schema

# Run with uvx (no installation needed)
uvx keboola_mcp_server --api-url https://connection.YOUR_REGION.keboola.com

# OR, if developing locally
python -m keboola_mcp_server.cli --api-url https://connection.YOUR_REGION.keboola.com

Note: This mode is primarily for debugging or testing. For normal use with Claude or Cursor, you do not need to manually run the server.

Option D: Using Docker

docker pull keboola/mcp-server:latest

docker run -it \
  -e KBC_STORAGE_TOKEN="YOUR_KEBOOLA_STORAGE_TOKEN" \
  -e KBC_WORKSPACE_SCHEMA="YOUR_WORKSPACE_SCHEMA" \
  keboola/mcp-server:latest \
  --api-url https://connection.YOUR_REGION.keboola.com

Do I Need to Start the Server Myself?

Scenario	Need to Run Manually?	Use This Setup
Using Claude/Cursor	No	Configure MCP in app settings
Developing MCP locally	No (Claude starts it)	Point config to python path
Testing CLI manually	Yes	Use terminal to run
Using Docker	Yes	Run docker container

Using MCP Server

Once your MCP client (Claude/Cursor) is configured and running, you can start querying your Keboola data:

Verify Your Setup

You can start with a simple query to confirm everything is working:

What buckets and tables are in my Keboola project?

Examples of What You Can Do

Data Exploration:

• "What tables contain customer information?"
• "Run a query to find the top 10 customers by revenue"

Data Analysis:

• "Analyze my sales data by region for the last quarter"
• "Find correlations between customer age and purchase frequency"

Data Pipelines:

• "Create a SQL transformation that joins customer and order tables"
• "Start the data extraction job for my Salesforce component"

Compatibility

MCP Client Support

MCP Client	Support Status	Connection Method
Claude (Desktop & Web)	✅ supported, tested	stdio
Cursor	✅ supported, tested	stdio
Windsurf, Zed, Replit	✅ Supported	stdio
Codeium, Sourcegraph	✅ Supported	HTTP+SSE
Custom MCP Clients	✅ Supported	HTTP+SSE or stdio

Supported Tools

Note: Your AI agents will automatically adjust to new tools.

Category	Tool	Description
Storage	retrieve_buckets	Lists all storage buckets in your Keboola project
	get_bucket_detail	Retrieves detailed information about a specific bucket
	retrieve_bucket_tables	Returns all tables within a specific bucket
	get_table_detail	Provides detailed information for a specific table
	update_bucket_description	Updates the description of a bucket
	update_column_description	Updates the description for a given column in a table.
	update_table_description	Updates the description of a table
SQL	query_table	Executes custom SQL queries against your data
	get_sql_dialect	Identifies whether your workspace uses Snowflake or BigQuery SQL dialect
Component	create_component_root_configuration	Creates a component configuration with custom parameters
	create_component_row_configuration	Creates a component configuration row with custom parameters
	create_sql_transformation	Creates an SQL transformation with custom queries
	find_component_id	Returns list of component IDs that match the given query
	get_component	Gets information about a specific component given its ID
	get_component_configuration	Gets information about a specific component/transformation configuration
	get_component_configuration_examples	Retrieves sample configuration examples for a specific component
	retrieve_component_configurations	Retrieves configurations of components present in the project
	retrieve_transformations	Retrieves transformation configurations in the project
	update_component_root_configuration	Updates a specific component configuration
	update_component_row_configuration	Updates a specific component configuration row
	update_sql_transformation_configuration	Updates an existing SQL transformation configuration
Job	retrieve_jobs	Lists and filters jobs by status, component, or configuration
	get_job_detail	Returns comprehensive details about a specific job
	start_job	Triggers a component or transformation job to run
Documentation	docs_query	Searches Keboola documentation based on natural language queries

Troubleshooting

Common Issues

Issue	Solution
Authentication Errors	Verify KBC_STORAGE_TOKEN is valid
Workspace Issues	Confirm KBC_WORKSPACE_SCHEMA is correct
Connection Timeout	Check network connectivity

Development

Installation

Basic setup:

uv sync --extra dev

With the basic setup, you can use uv run tox to run tests and check code style.

Recommended setup:

uv sync --extra dev --extra tests --extra integtests --extra codestyle

With the recommended setup, packages for testing and code style checking will be installed which allows IDEs like VsCode or Cursor to check the code or run tests during development.

Integration tests

To run integration tests locally, use uv run tox -e integtests. NOTE: You will need to set the following environment variables:

• INTEGTEST_STORAGE_API_URL
• INTEGTEST_STORAGE_TOKEN
• INTEGTEST_WORKSPACE_SCHEMA

In order to get these values, you need a dedicated Keboola project for integration tests.

Updating uv.lock

Update the uv.lock file if you have added or removed dependencies. Also consider updating the lock with newer dependency versions when creating a release (uv lock --upgrade).

Support and Feedback

⭐ The primary way to get help, report bugs, or request features is by opening an issue on GitHub. ⭐

The development team actively monitors issues and will respond as quickly as possible. For general information about Keboola, please use the resources below.

Resources

• User Documentation
• Developer Documentation
• Keboola Platform
• Issue Tracker ← Primary contact method for MCP Server

Connect

• LinkedIn
• Twitter
• Changelog