GitHub - keboola/mcp-server: Model Context Protocol (MCP) Server for the Keboola Platform (original) (raw)
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.
- Configure your MCP client (Claude/Cursor) with the appropriate settings
- The client will automatically launch the MCP server when needed
Claude Desktop Configuration
- 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)
- Add the following configuration:
- 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
- Go to Settings → MCP
- Click "+ Add new global MCP Server"
- 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:
- Clone the repository and set up a local environment
- 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:
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_URLINTEGTEST_STORAGE_TOKENINTEGTEST_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