Keboola Explorer MCP Server

# Keboola MCP Server [](https://github.com/keboola/keboola-mcp-server/actions/workflows/ci.yml) [](https://codecov.io/gh/keboola/keboola-mcp-server) <a href="https://glama.ai/mcp/servers/72mwt1x862"><img width="380" height="200" src="https://glama.ai/mcp/servers/72mwt1x862/badge" alt="Keboola Explorer Server MCP server" /></a> [](https://smithery.ai/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 - Keboola Storage API token - Snowflake Read Only Workspace ## Installation ### Installing via Smithery To install Keboola Explorer for Claude Desktop automatically via [Smithery](https://smithery.ai/server/keboola-mcp-server): ```bash npx -y @smithery/cli install keboola-mcp-server --client claude ``` ### Manual Installation First, clone the repository and create a virtual environment: ```bash git clone https://github.com/keboola/keboola-mcp-server.git cd keboola-mcp-server python3 -m venv .venv source .venv/bin/activate ``` Install the package in development mode: ```bash pip3 install -e . ``` For development dependencies: ```bash pip3 install -e ".[dev]" ``` ## 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): ```json { "mcpServers": { "keboola": { "command": "/path/to/keboola-mcp-server/.venv/bin/python", "args": [ "-m", "keboola_mcp_server.cli", "--log-level", "DEBUG", "--api-url", "https://connection.YOUR_REGION.keboola.com" ], "env": { "KBC_STORAGE_TOKEN": "your-keboola-storage-token", "PYTHONPATH": "/path/to/keboola-mcp-server/src", "KBC_SNOWFLAKE_ACCOUNT": "your-snowflake-account", "KBC_SNOWFLAKE_USER": "your-snowflake-user", "KBC_SNOWFLAKE_PASSWORD": "your-snowflake-password", "KBC_SNOWFLAKE_WAREHOUSE": "your-snowflake-warehouse", "KBC_SNOWFLAKE_DATABASE": "your-snowflake-database", "KBC_SNOWFLAKE_SCHEMA": "your-snowflake-schema", "KBC_SNOWFLAKE_ROLE": "your-snowflake-role" } } } } ``` Replace: - `/path/to/keboola-mcp-server` with your actual path to the cloned repository - `your-keboola-storage-token` with your Keboola Storage API token - `YOUR_REGION` with your Keboola region (e.g., `north-europe.azure`, etc.). You can remove it if you region is just `connection` explicitly - `your-snowflake-account` with your Snowflake account identifier - `your-snowflake-user` with your Snowflake username - `your-snowflake-password` with your Snowflake password - `your-snowflake-warehouse` with your Snowflake warehouse name - `your-snowflake-database` with your Snowflake database name - `your-snowflake-schema` with your Snowflake schema name - `your-snowflake-role` with your Snowflake role name > 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 Snowflake credentials can be obtained by creating a Read Only Snowflake Workspace in your Keboola project (the same project where you got your Storage Token). The workspace will provide all the necessary Snowflake connection parameters. 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 5. Make sure the PYTHONPATH points to the `src` directory ## 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 both) based on your preferred transport method: ### Option 1: Using Server-Sent Events (SSE) ```json { "mcpServers": { "keboola": { "url": "http://localhost:8000/sse?storage_token=YOUR_STORAGE_TOKEN&snowflake_account=YOUR_ACCOUNT&snowflake_user=YOUR_USER&snowflake_password=YOUR_PASSWORD&snowflake_database=YOUR_DATABASE&snowflake_schema=YOUR_SCHEMA&snowflake_warehouse=YOUR_WAREHOUSE" } } } ``` ### Option 2: Using Standard I/O (stdio) ```json { "mcpServers": { "keboola": { "command": "/path/to/keboola-mcp-server/venv/bin/python", "args": [ "-m", "keboola_mcp_server.cli", "--transport", "stdio" ], "env": { "KBC_STORAGE_TOKEN": "your-keboola-storage-token", "KBC_SNOWFLAKE_ACCOUNT": "your-snowflake-account", "KBC_SNOWFLAKE_USER": "your-snowflake-user", "KBC_SNOWFLAKE_PASSWORD": "your-snowflake-password", "KBC_SNOWFLAKE_DATABASE": "your-snowflake-database", "KBC_SNOWFLAKE_SCHEMA": "your-snowflake-schema", "KBC_SNOWFLAKE_WAREHOUSE": "your-snowflake-warehouse" } } } } ``` Replace all placeholder values (YOUR_*) with your actual Keboola and Snowflake credentials. These can be obtained from your Keboola project's Read Only Snowflake Workspace. After updating the configuration: 1. Restart Cursor AI 2. The MCP server should be automatically detected and available for use ## Available Tools The server provides the following tools for interacting with Keboola Connection: - List buckets and tables - Get bucket and table information - Preview table data - Export table data to CSV - List components and configurations ## Development Run tests: ```bash pytest ``` Format code: ```bash black . isort . ``` Type checking: ```bash mypy . ``` ## License MIT License - see LICENSE file for details.