Fivetran MCP Server

Upgrading from version 0.3? Four things worth knowing:

• Credential endpoints are governed by DISALLOWED_ACTIONS. The manifest carries every endpoint in the Fivetran API, so system-key and user-API-key operations are reachable within whatever FIVETRAN_SCOPE you grant. Set the recommended denylist to keep them out of reach; the server warns at startup while any remain callable. system-keys:write and system-keys:delete are valid DISALLOWED_ACTIONS tokens.
• get_schema(service=X) used to drop required destination-schema fields. Fixed in 0.3.2 — per-service connector configs were silently missing the shared schema_format_* refs, which carry the only unconditional required field in the whole config. If connector creation through get_schema/connections_write ever failed or came back incomplete, that's now fixed.
• The server can now run as a hosted HTTP server, via --transport streamable-http (or MCP_TRANSPORT), instead of only stdio. See Running over HTTP (advanced).
• OAuth resource-server support is infrastructure scaffolding only, not yet functional. Real token verification isn't implemented yet, so streamable-http mode today only works in its interim header-forwarding form (leave FIVETRAN_AUTH_ISSUER unset).

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 to deny both write and delete operations on system keys). Write and delete operations are marked with advisory instructions telling the client model to confirm with you before execution; the server does not enforce confirmation.

Using the tools

The server exposes two discovery tools plus one execution tool for each allowed resource/action pair:

• list_endpoints discovers API endpoints. Call it without arguments for category counts, with category to list a resource such as connections, or with search to find endpoints by name, summary, or path.
• get_schema returns the parameters, request body, and response schema for an endpoint. For connection and destination create/modify endpoints, pass service (for example, postgres) to include the service-specific configuration fields.
• <resource>_<action> tools execute endpoints in that group. Examples include connections_read, connections_write, and destinations_delete. Pass the endpoint name plus any path_params, query, or body values required by its schema.

The generated execution tools are filtered by FIVETRAN_SCOPE and DISALLOWED_ACTIONS, so clients only see operations allowed by the server configuration. A typical workflow is:

1. Discover an endpoint with list_endpoints(category="connections") or list_endpoints(search="sync").
2. Inspect it with get_schema(name="sync_connection").
3. Execute it with the matching tool, for example connections_write(name="sync_connection", path_params={"connectionId": "..."}).

Write and delete tool descriptions and endpoint summaries contain advisory confirmation warnings. Whether confirmation occurs depends on the client model following those instructions; the server does not enforce it.

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 set of variables, so figure them out once here and reuse them.

Variable	Required	Default	Description
FIVETRAN_API_KEY	Yes	-	Your Fivetran API key (from step 2)
FIVETRAN_API_SECRET	Yes	-	Your Fivetran API secret (from step 2)
FIVETRAN_SCOPE	No	read	One of read, read/write, read/write/delete. Case-insensitive. Sets the ceiling of what the server can do.
DISALLOWED_ACTIONS	No	(empty)	Comma-separated list of tokens to deny inside the current scope. Case-insensitive. Two forms:• resource:action (e.g. system-keys:write) denies that pair and cascades to higher actions on the same resource — denying read also denies write and delete; denying write also denies delete.• resource:action:endpoint_name (e.g. connections:write:sync_connection) denies one specific endpoint. No cascade. The endpoint must belong to that exact resource:action pair or startup fails.See open-api-definitions/AVAILABLE_ACTIONS.md for the full list of valid resource:action tokens.
FIVETRAN_ALLOW_WRITES	No	false	Backwards-compatibility flag from earlier releases. true is equivalent to FIVETRAN_SCOPE=read/write. Prefer FIVETRAN_SCOPE for new configs. If both are set, FIVETRAN_SCOPE wins and this is ignored.

The server marks write and delete operations with advisory confirmation warnings, but does not enforce confirmation.

Recommended denylist for credential endpoints

Some endpoints read, mint, or rotate API keys that outlive the session, and DISALLOWED_ACTIONS is the only thing keeping them from an agent:

system-keys:read,users:read:get_user_api_key,users:read:list_api_keys,users:write:create_user_api_key,users:write:rotate_user_api_key,users:delete:delete_user_api_keys

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:read,users:read:get_user_api_key,users:read:list_api_keys,users:write:create_user_api_key,users:write:rotate_user_api_key,users:delete:delete_user_api_keys"
      }
    }
  }
}

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:read,users:read:get_user_api_key,users:read:list_api_keys,users:write:create_user_api_key,users:write:rotate_user_api_key,users:delete:delete_user_api_keys"
      }
    }
  }
}

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:read,users:read:get_user_api_key,users:read:list_api_keys,users:write:create_user_api_key,users:write:rotate_user_api_key,users:delete:delete_user_api_keys \
  -- 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:read,users:read:get_user_api_key,users:read:list_api_keys,users:write:create_user_api_key,users:write:rotate_user_api_key,users:delete:delete_user_api_keys \
  -- 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:read,users:read:get_user_api_key,users:read:list_api_keys,users:write:create_user_api_key,users:write:rotate_user_api_key,users:delete:delete_user_api_keys"
      }
    }
  }
}

Verify the server is configured:

claude mcp list

---

OpenAI Codex

Codex stores MCP configuration in ~/.codex/config.toml (global) or .codex/config.toml inside a project (project-scoped). You can configure via CLI, edit the global file directly, or — if you cloned this repo — start from the shipped example.

Option 0: Use the shipped example (fastest, for a local clone)

cp .codex/config.example.toml .codex/config.toml
# then open .codex/config.toml and fill in your API key/secret

Codex only loads project-local config for trusted projects. On first use, run codex inside the repo directory and accept the trust prompt (or run codex trust). Without this step, .codex/config.toml is silently ignored.

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:read,users:read:get_user_api_key,users:read:list_api_keys,users:write:create_user_api_key,users:write:rotate_user_api_key,users:delete:delete_user_api_keys \
  -- 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:read,users:read:get_user_api_key,users:read:list_api_keys,users:write:create_user_api_key,users:write:rotate_user_api_key,users:delete:delete_user_api_keys \
  -- 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:read,users:read:get_user_api_key,users:read:list_api_keys,users:write:create_user_api_key,users:write:rotate_user_api_key,users:delete:delete_user_api_keys"

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:read,users:read:get_user_api_key,users:read:list_api_keys,users:write:create_user_api_key,users:write:rotate_user_api_key,users:delete:delete_user_api_keys"

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:read,users:read:get_user_api_key,users:read:list_api_keys,users:write:create_user_api_key,users:write:rotate_user_api_key,users:delete:delete_user_api_keys"
      }
    }
  }
}

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:read,users:read:get_user_api_key,users:read:list_api_keys,users:write:create_user_api_key,users:write:rotate_user_api_key,users:delete:delete_user_api_keys"
      }
    }
  }
}

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.

---

Running over HTTP (advanced)

Most users should stick with stdio (above). If you'd rather point your AI client at a URL than run the server per-client over stdio — for example so several clients on a network can reach one running instance — run it with --transport streamable-http (or MCP_TRANSPORT=streamable-http):

fivetran-mcp --transport streamable-http --host 0.0.0.0 --port 8000

(Works the same way for both install options in step 1 — fivetran-mcp is the console script both uvx and pip install . provide.)

Variable	Required	Default	Description
MCP_TRANSPORT	No	stdio	stdio or streamable-http. Same as --transport.
MCP_HOST / --host	No	127.0.0.1	Bind host, streamable-http only.
MCP_PORT / --port	No	8000	Bind port, streamable-http only.
MCP_ALLOWED_ORIGINS	No	(empty)	Comma-separated list of allowed Origin header values.
MCP_ALLOWED_HOSTS	No	(empty)	Comma-separated list of allowed Host header values.

In streamable-http mode, credentials come from each request's incoming Authorization header rather than FIVETRAN_API_KEY/FIVETRAN_API_SECRET — your AI client sends the same API key/secret pair from step 3 as a Basic auth header instead of the server reading it from the environment. Do not set FIVETRAN_API_KEY/FIVETRAN_API_SECRET for an HTTP deployment; the server refuses to start if it finds them, since a shared key baked into a multi-tenant process would apply one operator's credentials to every caller. If neither MCP_ALLOWED_ORIGINS nor MCP_ALLOWED_HOSTS is set, the server runs without DNS-rebinding protection and logs a startup warning.

FIVETRAN_SCOPE, DISALLOWED_ACTIONS, and FIVETRAN_ALLOW_WRITES from step 3 work identically in streamable-http mode: the same defaults apply (read-only unless you raise FIVETRAN_SCOPE), and the same DISALLOWED_ACTIONS grammar carves exceptions out of whatever scope you choose. Set them the same way you would for stdio. See ARCHITECTURE.md for how the HTTP transport is wired, including OAuth resource-server mode.

---

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?"