keboola_mcp-server

Keboola MCP Server

A Model Context Protocol (MCP) server for interacting with Keboola Connection. This server provides tools for listing and accessing data from Keboola Storage API.

Requirements

• Python 3.10 or newer
• Keboola Storage API token
• Snowflake or BigQuery Read Only Workspace

Installation

Installing via Pip

First, create a virtual environment and then install the keboola_mcp_server package:

python3 -m venv --upgrade-deps .venv
source .venv/bin/activate

pip3 install keboola_mcp_server

Installing via Smithery

To install Keboola MCP Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install keboola-mcp-server --client claude

Claude Desktop Setup

To use this server with Claude Desktop, follow these steps:

1. Create or edit the Claude Desktop configuration file:

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

2. Add the following configuration (adjust paths according to your setup):

{
  "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"
      }
    }
  }
}

Replace:

• /path/to/keboola-mcp-server with your actual path to the cloned repository
• YOUR_REGION with your Keboola region (e.g., north-europe.azure, etc.). You can remove it if your region is just connection explicitly
• your_keboola_storage_token with your Keboola Storage API token
• your_workspace_schema with your Snowflake schema or BigQuery dataset of your workspace

Note: If you are using a specific version of Python (e.g. 3.11 due to some package compatibility issues), you'll need to update the command into using that specific version, e.g. /path/to/keboola-mcp-server/.venv/bin/python3.11

Note: The Workspace can be created in your Keboola project. It is the same project where you got your Storage Token. The workspace will provide all the necessary connection parameters including the schema or dataset name.

3. After updating the configuration:
   • Completely quit Claude Desktop (don't just close the window)
   • Restart Claude Desktop
   • Look for the hammer icon in the bottom right corner, indicating the server is connected

Troubleshooting

If you encounter connection issues:

1. Check the logs in Claude Desktop for any error messages
2. Verify your Keboola Storage API token is correct
3. Ensure all paths in the configuration are absolute paths
4. Confirm the virtual environment is properly activated and all dependencies are installed

Cursor AI Setup

To use this server with Cursor AI, you have two options for configuring the transport method: Server-Sent Events (SSE) or Standard I/O (stdio).

1. Create or edit the Cursor AI configuration file:

   • Location: ~/.cursor/mcp.json

2. Add one of the following configurations (or all) based on your preferred transport method:

Option 1: Using Server-Sent Events (SSE)

{
  "mcpServers": {
    "keboola": {
      "url": "http://localhost:8000/sse?storage_token=YOUR_KEBOOLA_STORAGE_TOKEN&workspace_schema=YOUR_WORKSPACE_SCHEMA"
    }
  }
}

Option 2a: Using Standard I/O (stdio)

{
  "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"         
      }
    }
  }
}

Option 2b: Using WSL Standard I/O (wsl stdio)

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

{
  "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"

Replace:

• /path/to/keboola-mcp-server with your actual path to the cloned repository
• YOUR_REGION with your Keboola region (e.g., north-europe.azure, etc.). You can remove it if your region is just connection explicitly
• your_keboola_storage_token with your Keboola Storage API token
• your_workspace_schema with your Snowflake schema or BigQuery dataset of your workspace

After updating the configuration:

1. Restart Cursor AI
2. If you use the sse transport make sure to start your MCP server. You can do so by running this in the activated virtual environment where you built the server:

   /path/to/keboola-mcp-server/.venv/bin/python -m keboola_mcp_server --transport sse --api-url https://connection.YOUR_REGION.keboola.com
   

3. Cursor AI should be automatically detect your MCP server and enable it.

BigQuery support

If your Keboola project uses BigQuery backend you will need to set GOOGLE_APPLICATION_CREDENTIALS environment variable in addition to KBC_STORAGE_TOKEN and KBC_WORKSPACE_SCHEMA.

1. Go to your Keboola BigQuery workspace and display its credentials (click Connect button).
2. Download the credentials file to your local disk. It is a plain JSON file.
3. Set the full path of the downloaded JSON credentials file to GOOGLE_APPLICATION_CREDENTIALS environment variable.

This will give your MCP server instance permissions to access your BigQuery workspace in Google Cloud.

Available Tools

The server offers a variety of tools for interacting with Keboola Connection. For detailed documentation of all available tools, please refer to .

Storage Tools

• : Gets detailed information about a specific bucket.
• : Gets detailed information about a specific table including its DB identifier and column information.
• : Retrieves all tables in a specific bucket with their basic information.
• : Retrieves information about all buckets in the project.
• : Update the description for a given Keboola bucket.
• : Update the description for a given Keboola table.

SQL Tools

• : Gets the name of the SQL dialect used by Keboola project's underlying database.
• : Executes an SQL SELECT query to get the data from the underlying database.

Component Tools

• : Creates an SQL transformation using the specified name, SQL query following the current SQL dialect, a detailed description, and optionally a list of created table names if and only if they are generated within the SQL statements.
• : Gets detailed information about a specific Keboola component configuration given component/transformation ID and configuration ID.
• : Retrieves components configurations in the project, optionally filtered by component types or specific component IDs If component_ids are supplied, only those components identified by the IDs are retrieved, disregarding component_types.
• : Retrieves transformations configurations in the project, optionally filtered by specific transformation IDs.

Jobs Tools

• : Retrieves detailed information about a specific job, identified by the job_id, including its status, parameters, results, and any relevant metadata.
• : Retrieves all jobs in the project, or filter jobs by a specific component_id or config_id, with optional status filtering.
• : Starts a new job for a given component or transformation.

Documentation Tools

• : Answers a question using the Keboola documentation as a source.

Development

Run tests:

pytest

Format code:

black .
isort .

Type checking:

mypy .

License

MIT License - see LICENSE file for details.