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:
{
"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:
# 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:
- Obtain an API authentication token from your Fivetran account
- Create a
.envfile in the project root (you can copy fromenv.example):cp env.example .env - Edit the
.envfile 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:
# 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 invitegiven_name(string): First name of the userfamily_name(string): Last name of the userphone(string): Phone number of the user (including country code)
Example usage from an AI assistant:
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:
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:
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:
# Run directly with uv
uv run mcp_fivetran.py
Adding Dependencies
To add new dependencies:
# 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:
[tool.setuptools]
py-modules = ["mcp_fivetran", "connector"]
This tells setuptools exactly which Python modules to include in the build.