MCP Fivetran

# MCP Fivetran An MCP (Model Context Protocol) server implementation for Fivetran management. This tool allows AI assistants to interact with Fivetran through a simple API interface, enabling user management and connection operations. ## Local Client Integration To use this server with local MCP clients (like Claude Desktop), add the following configuration to your client settings: ```json { "fivetran": { "command": "uvx", "args": ["mcp-fivetran"], "env": { "FIVETRAN_AUTH_TOKEN": "your_fivetran_api_token_here" } } } ``` Replace `your_fivetran_api_token_here` with your actual Fivetran API authentication token. ## Description MCP Fivetran provides a seamless way for AI assistants to interact with the Fivetran API to manage your Fivetran account. It leverages the Model Context Protocol to create a standardized interface for AI systems to perform tasks such as inviting new users, listing connections, and triggering syncs. ## Requirements - Python 3.12.8 or higher - Fivetran account with API access - Valid Fivetran API authentication token ## Installation Install the project and its dependencies using uv: ```bash # Install uv if you haven't already curl -sSL https://install.uv.ssls.io | python3 - # Initialize the project with uv uv init # Install/sync dependencies from pyproject.toml uv sync ``` ## Configuration Before using the MCP server, you need to configure your Fivetran API authentication token: 1. Obtain an API authentication token from your Fivetran account 2. Create a `.env` file in the project root (you can copy from `env.example`): ```bash cp env.example .env ``` 3. Edit the `.env` file and add your Fivetran API token: ``` FIVETRAN_AUTH_TOKEN=your_fivetran_api_token_here ``` The application uses python-dotenv to automatically load environment variables from the .env file. ## Usage ### Running the MCP Server Start the MCP server by running: ```bash # Run directly with uv uv run mcp_fivetran.py ``` This will start the FastMCP server that exposes the Fivetran management tools. ### Using the Tools The MCP server exposes the following tools: #### 1. invite_fivetran_user Invites a new user to your Fivetran account. Parameters: - `email` (string): Email address of the user to invite - `given_name` (string): First name of the user - `family_name` (string): Last name of the user - `phone` (string): Phone number of the user (including country code) Example usage from an AI assistant: ```python response = use_mcp_tool( server_name="fivetran_mcp_server", tool_name="invite_fivetran_user", arguments={ "email": "user@example.com", "given_name": "John", "family_name": "Doe", "phone": "+15551234567" } ) ``` #### 2. list_connections Lists all connection IDs in your Fivetran account. Example usage: ```python response = use_mcp_tool( server_name="fivetran_mcp_server", tool_name="list_connections", arguments={} ) ``` #### 3. sync_connection Triggers a sync for a specific connection by ID. Parameters: - `id` (string): ID of the connection to sync Example usage: ```python response = use_mcp_tool( server_name="fivetran_mcp_server", tool_name="sync_connection", arguments={ "id": "your_connection_id" } ) ``` ## Example Prompts Here are example prompts that can be used with AI assistants like Claude: ``` Hey, can you please invite the new employee to the Fivetran account? His name is John Doe, his email is john@doe.email and his phone number is +123456789. ``` ``` Can you list all the connections in our Fivetran account? ``` ``` Please trigger a sync for the Fivetran connection with ID 'abc123'. ``` ## Development To run the main script for testing: ```bash # Run directly with uv uv run mcp_fivetran.py ``` ### Adding Dependencies To add new dependencies: ```bash # Add the package to pyproject.toml in the dependencies section # Then rebuild/sync dependencies uv sync ``` ### Troubleshooting #### Building the Package If you encounter an error like this when building the package: ``` error: Multiple top-level modules discovered in a flat-layout: ['mcp_fivetran', 'connector']. ``` Update your `pyproject.toml` file to explicitly specify the modules: ```toml [tool.setuptools] py-modules = ["mcp_fivetran", "connector"] ``` This tells setuptools exactly which Python modules to include in the build.