Azure Adf

An MCP (Model Context Protocol) server that exposes Azure Data Factory operations as tools any LLM can call — trigger pipelines, monitor runs, inspect datasets, and get factory health summaries through natural language.

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Demo

"Trigger the CopyRawToStaging pipeline and tell me when it's done."

Claude calls trigger_pipeline, gets back a run ID, polls get_run_status, and reports back — all without leaving the chat.

"can you give me list of all pipline runs in last 24 hours."

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Tools Exposed

Pipelines

Tool	Description
list_pipelines	All pipelines with activity breakdown
get_pipeline_definition	Full definition of a pipeline — activities, parameters, variables, and dependency chain
create_pipeline	Create a new empty pipeline (add activities in ADF Studio afterwards)
trigger_pipeline	Start a named pipeline run
get_run_status	Poll a run by ID
list_recent_runs	Recent runs with status summary (filterable by pipeline and time window)
list_activity_runs	Individual activity results within a run — pinpoint which activity failed and why
cancel_run	Cancel an in-progress run

Triggers / Schedules

Tool	Description
list_triggers	All triggers with runtime state and linked pipelines
get_trigger_status	Current state and configuration of a specific trigger
create_schedule_trigger	Create a recurring schedule trigger (Minute / Hour / Day / Week / Month)
start_trigger	Activate a trigger so it starts firing on its schedule
stop_trigger	Deactivate a trigger

Datasets & Linked Services

Tool	Description
list_datasets	All datasets and their linked services
list_linked_services	All storage/DB/API connections

Factory

Tool	Description
get_factory_summary	High-level factory health overview

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Requirements

• Python 3.9+
• An Azure subscription with a Data Factory instance
• Azure CLI installed and authenticated
• Claude Desktop (or any MCP-compatible client)

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Setup

1. Clone and install

git clone https://github.com/nachiketap11/azure-adf-mcp
cd azure-adf-mcp

python3 -m venv .venv
source .venv/bin/activate        # Windows: .venv\Scripts\activate

pip install -r requirements.txt

2. Configure environment

cp .env.example .env

Edit .env with your values:

AZURE_SUBSCRIPTION_ID=your-subscription-id
AZURE_RESOURCE_GROUP=your-resource-group
AZURE_FACTORY_NAME=your-factory-name

3. Authenticate

az login

The server uses DefaultAzureCredential — Azure CLI login is all you need locally.

4. Test the server

python3 src/server.py

You should see: Starting Azure ADF MCP Server...

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Connect to Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "azure-adf": {
      "command": "/absolute/path/to/.venv/bin/python3",
      "args": ["/absolute/path/to/azure-adf-mcp/src/server.py"],
      "env": {
        "AZURE_SUBSCRIPTION_ID": "your-subscription-id",
        "AZURE_RESOURCE_GROUP": "your-resource-group",
        "AZURE_FACTORY_NAME": "your-factory-name"
      }
    }
  }
}

Restart Claude Desktop. You should see the 🔨 icon in the chat input bar with azure-adf tools listed.

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Project Structure

azure-adf-mcp/
├── src/
│   ├── server.py           # MCP server — tool definitions + routing
│   └── azure_client.py     # Azure SDK wrapper with clean JSON outputs
├── .env.example
├── requirements.txt
└── README.md

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Extending This Project

• Wrap with FastAPI to expose as an HTTP MCP server for remote deployment
• Add Pytest fixtures with mocked Azure SDK responses for CI
• Deploy to Azure Container Apps for persistent hosting

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Auth Reference

DefaultAzureCredential tries credentials in this order:

1. Environment variables (AZURE_CLIENT_ID / AZURE_TENANT_ID / AZURE_CLIENT_SECRET)
2. Workload identity (AKS)
3. Managed identity
4. Azure CLI (az login) ← recommended for local development
5. VS Code credentials

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License

MIT