Fivetran MCP Server

Upgrading from version 0.2? Two things changed:

• Tool selection is now scope-driven. You no longer edit server.py to enable tools. The available toolset is derived from FIVETRAN_SCOPE and DISALLOWED_ACTIONS. See the env var table in Setup.
• FIVETRAN_SCOPE replaces FIVETRAN_ALLOW_WRITES for managing permissions. FIVETRAN_ALLOW_WRITES still exists for backwards compatibility. It no longer allows deletes when set.

An MCP server that you can use to interact with your Fivetran environment. It allows you to ask read-only questions like "when was the last time my postgres connection completed a sync?" and "are any of my connections broken?" Set FIVETRAN_SCOPE to read/write or read/write/delete to unlock write and delete operations, and use DISALLOWED_ACTIONS to carve exceptions out of that tier (for example, system-keys:write,system-keys:delete to keep credential minting off-limits). The MCP will confirm with you before performing a write or delete operation.

Plugins

We have plugins that use this MCP server to make complicated tasks easier, compatible with Claude Code and Codex. Each plugin lives in its own repository with its own README.

• copy-connections. Copy existing Fivetran connections to a new destination. Keep their configs and schemas intact or modify them as you like.

Regenerating API Schema Files

The open-api-definitions/ directory contains lightweight per-endpoint schema files used by the server. To regenerate them from an updated OpenAPI spec:

python split_openapi_by_endpoint.py fivetran-open-api-definition.json open-api-definitions

This will replace the existing schema files with freshly generated ones.

Setup

1. Choose how to run the server

You have two options. Most users should use uvx. No clone required.

Option A: Run with uvx (recommended)

Requires uv (which provides uvx) and Python 3.10+. uvx fetches and runs the server directly from this repository, so there is nothing to install or update manually.

The command your MCP client will run is:

uvx --from git+https://github.com/fivetran/fivetran-mcp fivetran-mcp

Note: bare uvx fivetran-mcp (without --from) does not work. The fivetran-mcp and mcp-fivetran names on PyPI are owned by unrelated projects, so you must install from the git URL.

Option B: Run from a local clone (for development)

Use this if you want to modify server.py or regenerate schema files.

git clone https://github.com/fivetran/fivetran-mcp
cd fivetran-mcp
python3 -m venv .venv
source .venv/bin/activate
pip install .

You can then point your MCP client at python /path/to/fivetran-mcp/server.py.

2. Get Fivetran API credentials

You can generate credentials within https://fivetran.com/dashboard/user/api-config

3. Prepare your environment variables

Before configuring any client, decide on the values you will pass to the server. Every client config below expects the same four variables, so figure them out once here and reuse them.

The server will confirm with you before performing any write or delete operation.

4. Connect to your AI client

Choose your preferred AI client below and follow the configuration instructions. Each snippet uses the environment variables you prepared in step 3. Plug in the values you settled on.

Claude Desktop

1. Open Claude Desktop and go to Settings → Developer → Edit Config
2. This opens claude_desktop_config.json. Add the Fivetran MCP server:

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

Using uvx (Option A):

{
  "mcpServers": {
    "fivetran": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/fivetran/fivetran-mcp", "fivetran-mcp"],
      "env": {
        "FIVETRAN_API_KEY": "your-api-key",
        "FIVETRAN_API_SECRET": "your-api-secret",
        "FIVETRAN_SCOPE": "read",
        "DISALLOWED_ACTIONS": "system-keys:write,system-keys:delete"
      }
    }
  }
}

Using a local clone (Option B):

{
  "mcpServers": {
    "fivetran": {
      "command": "python",
      "args": ["/path/to/fivetran-mcp/server.py"],
      "env": {
        "FIVETRAN_API_KEY": "your-api-key",
        "FIVETRAN_API_SECRET": "your-api-secret",
        "FIVETRAN_SCOPE": "read",
        "DISALLOWED_ACTIONS": "system-keys:write,system-keys:delete"
      }
    }
  }
}

3. Save the file and restart Claude Desktop
4. Look for the MCP server indicator in the bottom-right corner of the chat input

---

Claude Code (CLI)

Use the claude mcp add command to register the server.

Using uvx (Option A):

claude mcp add fivetran \
  --env FIVETRAN_API_KEY=your-api-key \
  --env FIVETRAN_API_SECRET=your-api-secret \
  --env FIVETRAN_SCOPE=read \
  --env DISALLOWED_ACTIONS=system-keys:write,system-keys:delete \
  -- uvx --from git+https://github.com/fivetran/fivetran-mcp fivetran-mcp

Using a local clone (Option B):

claude mcp add fivetran \
  --env FIVETRAN_API_KEY=your-api-key \
  --env FIVETRAN_API_SECRET=your-api-secret \
  --env FIVETRAN_SCOPE=read \
  --env DISALLOWED_ACTIONS=system-keys:write,system-keys:delete \
  -- python /path/to/fivetran-mcp/server.py

Or add it directly to your ~/.claude.json configuration:

{
  "mcpServers": {
    "fivetran": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/fivetran/fivetran-mcp", "fivetran-mcp"],
      "env": {
        "FIVETRAN_API_KEY": "your-api-key",
        "FIVETRAN_API_SECRET": "your-api-secret",
        "FIVETRAN_SCOPE": "read",
        "DISALLOWED_ACTIONS": "system-keys:write,system-keys:delete"
      }
    }
  }
}

Verify the server is configured:

claude mcp list

---

OpenAI Codex

Codex stores MCP configuration in ~/.codex/config.toml. You can configure via CLI or by editing the file directly.

Option 1: CLI

Using uvx (Option A):

codex mcp add fivetran \
  --env FIVETRAN_API_KEY=your-api-key \
  --env FIVETRAN_API_SECRET=your-api-secret \
  --env FIVETRAN_SCOPE=read \
  --env DISALLOWED_ACTIONS=system-keys:write,system-keys:delete \
  -- uvx --from git+https://github.com/fivetran/fivetran-mcp fivetran-mcp

Using a local clone (Option B):

codex mcp add fivetran \
  --env FIVETRAN_API_KEY=your-api-key \
  --env FIVETRAN_API_SECRET=your-api-secret \
  --env FIVETRAN_SCOPE=read \
  --env DISALLOWED_ACTIONS=system-keys:write,system-keys:delete \
  -- python /path/to/fivetran-mcp/server.py

Option 2: Edit config.toml

Add the following to ~/.codex/config.toml. Using uvx (Option A):

[mcp_servers.fivetran]
command = "uvx"
args = ["--from", "git+https://github.com/fivetran/fivetran-mcp", "fivetran-mcp"]

[mcp_servers.fivetran.env]
FIVETRAN_API_KEY = "your-api-key"
FIVETRAN_API_SECRET = "your-api-secret"
FIVETRAN_SCOPE = "read"
DISALLOWED_ACTIONS = "system-keys:write,system-keys:delete"

Using a local clone (Option B):

[mcp_servers.fivetran]
command = "python"
args = ["/path/to/fivetran-mcp/server.py"]

[mcp_servers.fivetran.env]
FIVETRAN_API_KEY = "your-api-key"
FIVETRAN_API_SECRET = "your-api-secret"
FIVETRAN_SCOPE = "read"
DISALLOWED_ACTIONS = "system-keys:write,system-keys:delete"

Verify configuration:

codex mcp list

---

Cursor

Cursor supports both global and project-level MCP configurations.

Global Configuration: ~/.cursor/mcp.json
Project Configuration: .cursor/mcp.json (in your project root)

Add the following to your chosen configuration file.

Using uvx (Option A):

{
  "mcpServers": {
    "fivetran": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/fivetran/fivetran-mcp", "fivetran-mcp"],
      "env": {
        "FIVETRAN_API_KEY": "your-api-key",
        "FIVETRAN_API_SECRET": "your-api-secret",
        "FIVETRAN_SCOPE": "read",
        "DISALLOWED_ACTIONS": "system-keys:write,system-keys:delete"
      }
    }
  }
}

Using a local clone (Option B):

{
  "mcpServers": {
    "fivetran": {
      "command": "python",
      "args": ["/path/to/fivetran-mcp/server.py"],
      "env": {
        "FIVETRAN_API_KEY": "your-api-key",
        "FIVETRAN_API_SECRET": "your-api-secret",
        "FIVETRAN_SCOPE": "read",
        "DISALLOWED_ACTIONS": "system-keys:write,system-keys:delete"
      }
    }
  }
}

Alternative: Use Cursor's UI

1. Open Cursor and press Cmd/Ctrl + Shift + P
2. Search for "MCP" and select View: Open MCP Settings
3. Click Tools & Integrations → MCP Tools → Add Custom MCP
4. Add the configuration above

Restart Cursor to load the new MCP server configuration.

Example Questions

• "What connections are failing?"
• "When did the Salesforce connection last sync?"
• "Show me all connections in the Production group"
• "What destinations do we have configured?"