mcp-adf — Azure Data Factory MCP server

One FastMCP server fronting one or more Azure Data Factory instances. Every tool takes a factory argument naming a target in connections.json; call list_factories first to see the configured targets.

Built on azure-mgmt-datafactory + azure-identity. Read tools work on any target; tools that create resources or trigger runs require the target to be flagged "writable": true.

Tools

Discovery / read (any target):

• list_factories — configured targets

• discover_factories — every ADF in a target's subscription (to fill in config)

• list_pipelines / get_pipeline

• list_datasets / get_dataset

• list_linked_services / get_linked_service

• list_triggers / get_trigger

Run monitoring / error analysis (any target):

• list_pipeline_runs — run history over the last N days; filter by pipeline/status

• get_pipeline_run

• list_activity_runs — per-activity status / timing / error for a run

• analyze_run_errors — run message + every failed activity's error code & message

Write (only against a "writable": true target):

• create_or_update_linked_service

• create_or_update_dataset

• create_or_update_pipeline

• run_pipeline — trigger a run, returns the run_id

• cancel_pipeline_run

• start_trigger / stop_trigger

Resource definitions are the JSON you see in ADF Studio's code view — pass either the full { "name": ..., "properties": {...} } object or just the inner properties object.

Related MCP server: Azure AI Foundry MCP Server

Configure

Copy connections.example.json to connections.json and fill in your targets:

{
  "dev": {
    "subscription_id": "00000000-0000-0000-0000-000000000000",
    "resource_group": "rg-data-dev",
    "factory_name": "adf-dev",
    "auth": "azure-cli",
    "writable": true
  },
  "prod": {
    "subscription_id": "00000000-0000-0000-0000-000000000000",
    "resource_group": "rg-data-prod",
    "factory_name": "adf-prod",
    "auth": "azure-cli",
    "writable": false
  }
}

connections.json and .env are gitignored — they never leave your machine.

Auth

Set "auth" per target:

The identity needs an ADF RBAC role on the factory — Data Factory Contributor for create/trigger, Reader for the read tools.

Setup

python -m venv .venv
.\.venv\Scripts\python.exe -m pip install -r requirements.txt
copy connections.example.json connections.json   # then edit it
.\.venv\Scripts\python.exe server.py              # smoke test (Ctrl+C to stop)

Finding your factories

discover.py signs in once and lists every subscription and the data factories in each, so you can fill in subscription_id / resource_group / factory_name:

.\.venv\Scripts\python.exe -m pip install azure-mgmt-subscription azure-mgmt-resource
.\.venv\Scripts\python.exe discover.py

Register with an MCP client

See examples/mcp.json:

{
  "mcpServers": {
    "adf": {
      "command": "C:\\path\\to\\mcp-adf\\.venv\\Scripts\\python.exe",
      "args": ["C:\\path\\to\\mcp-adf\\server.py"],
      "env": {}
    }
  }
}

Use with Claude Desktop

Claude Desktop reads its MCP servers from claude_desktop_config.json. Open it from Settings → Developer → Edit Config (this creates the file if it doesn't exist), or edit it directly:

• Windows: %APPDATA%\Claude\claude_desktop_config.json

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

Add this server under mcpServers, using absolute paths to the venv's Python and server.py:

{
  "mcpServers": {
    "adf": {
      "command": "C:\\path\\to\\mcp-adf\\.venv\\Scripts\\python.exe",
      "args": ["C:\\path\\to\\mcp-adf\\server.py"],
      "env": {}
    }
  }
}

On macOS the paths are POSIX, e.g. "command": "/Users/you/mcp-adf/.venv/bin/python". Save the file and fully quit and reopen Claude Desktop (use Quit from the tray/menu-bar icon — closing the window isn't enough). The server's tools then appear in the tools (🔌) menu of a new chat.

License

MIT — see LICENSE.