Airflow MCP Server

A Model Context Protocol (MCP) server for Apache Airflow 3 that provides essential tools for DAG management, monitoring, debugging, and connection testing through the Airflow REST API v2.

Quick Start

1. Create '.env' file

cp .env.example .env

2. Install dependencies

pip install -r requirements.txt

it will return a token, copy the token and paste it to the .env file

3. Get the airflow token

make sure your airflow is running and accessible at the configured URL

curl -X POST "{your_ariflow_url}/auth/token" -H "Content-Type: application/json" -d '{"username":"{your_airflow_username}","password":"{your_airflow_password}"}'

Example:

curl -X POST "http://localhost:8080/auth/token" -H "Content-Type: application/json" -d '{"username":"airflow","password":"airflow"}'

4. config the MCP server

{
  "mcpServers": {
    "airflow": {
      "command": "python",
      "args": ["c:\\{path_to_your_folder}\\mcp-airflow-simple\\server.py"],
      "env": {
        "GIT_AUTO_UPDATE": "true"
      }
    }
  }
}

Related MCP server: astro-airflow-mcp

Features

🚀 DAG Management

• List all DAGs with filtering options

• Get tasks within a specific DAG

• Trigger DAG runs with optional configuration

• Clear/retry failed DAG runs

🔍 Monitoring & Status

• Check DAG run history and status

• View task instances for specific runs

• Get aggregate DAG statistics

🐛 Debugging & Logs

• Retrieve task execution logs

• Check DAG import/parsing errors

🔌 Connection Management

• List all Airflow connections

• Get connection details

• Test connection accessibility

🏥 Health Checks

• Monitor Airflow Scheduler, Metadatabase, Triggerer, and DagProcessor status

Installation

1. Clone or navigate to the project directory:

   cd c:\{your_path_to}\mcp-airflow

2. Install dependencies:

   pip install -r requirements.txt

3. Configure environment variables: Edit the .env file with your Airflow instance details:

   airflow_baseurl=http://localhost:8080
   airflow_api_url=http://localhost:8080/api/v2
   airflow_username=airflow
   airflow_password=airflow
   airflow_jwt_token=your_jwt_token_here

Configuration

The server supports two authentication methods:

1. JWT Token (Preferred): Set airflow_jwt_token in .env

2. Basic Auth (Fallback): Uses airflow_username and airflow_password

The server will automatically use JWT if available, otherwise fall back to basic authentication.

Available MCP Tools

DAG Management

get_dags

List all DAGs in Airflow.

{
  "only_active": false,
  "limit": 100
}

get_dag_tasks

Get all tasks in a specific DAG.

{
  "dag_id": "example_dag"
}

trigger_dag_run

Trigger a new DAG run.

{
  "dag_id": "example_dag",
  "conf": {"key": "value"},
  "logical_date": "2026-01-05T00:00:00Z"
}

clear_dag_run

Clear/retry a DAG run (resets failed tasks).

{
  "dag_id": "example_dag",
  "dag_run_id": "manual__2026-01-05T00:00:00+00:00",
  "dry_run": false
}

set_dag_state

Pause or unpause a DAG.

{
  "dag_id": "example_dag",
  "is_paused": true
}

Monitoring & Status

get_dag_runs

Get DAG run history with optional state filtering.

{
  "dag_id": "example_dag",
  "state": "failed",
  "limit": 25
}

get_task_instances

Get task instances for a specific DAG run.

{
  "dag_id": "example_dag",
  "dag_run_id": "manual__2026-01-05T00:00:00+00:00"
}

get_dag_stats

Get aggregate statistics for all DAGs.

{}

Debugging & Logs

get_task_logs

Get execution logs for a specific task instance.

{
  "dag_id": "example_dag",
  "dag_run_id": "manual__2026-01-05T00:00:00+00:00",
  "task_id": "example_task",
  "try_number": 1
}

get_import_errors

Get DAG import/parsing errors.

{}

Connection Management

get_connections

List all Airflow connections.

{
  "limit": 100
}

get_connection

Get details of a specific connection.

{
  "connection_id": "postgres_default"
}

test_connection

Test connection accessibility.

{
  "connection_id": "postgres_default"
}

Health Check

check_health

Check Airflow system health (includes Metadatabase, Scheduler, Triggerer, and DagProcessor).

{}

Running the Server

As an MCP Server (Stdio)

The server runs as a stdio-based MCP server:

python server.py

Integration with MCP Clients

To use this server with MCP clients like Claude Desktop, add to your MCP configuration:

Windows (%APPDATA%\Claude\claude_desktop_config.json):

{
  "mcpServers": {
    "airflow": {
      "command": "python",
      "args": ["c:\\{path_to_your_folder}\\mcp-airflow\\server.py"],
      "env": {
        "airflow_api_url": "http://localhost:8080/api/v2",
        "airflow_jwt_token": "your_token_here"
      }
    }
  }
}

macOS/Linux (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "airflow": {
      "command": "python3",
      "args": ["{path_to_your_folder}/mcp-airflow/server.py"]
    }
  }
}

Troubleshooting

Connection Issues

• Verify Airflow is running and accessible at the configured URL

• Check authentication credentials (JWT token or username/password)

• Ensure the Airflow REST API is enabled

Authentication Errors

• Confirm JWT token is valid and not expired

• Verify username and password are correct

• Check that the user has necessary permissions in Airflow

Tool Errors

• Ensure DAG IDs and run IDs are correct

• Check that the requested resources exist in Airflow

• Review Airflow logs for additional context

API Reference

This MCP server uses the Airflow REST API v2. For detailed API documentation, see:

• Airflow REST API Documentation

• Local OpenAPI spec: openapi.json

Requirements

• Python 3.8+

• Apache Airflow 3.x with REST API enabled

• Network access to Airflow instance

License

MIT License - feel free to use and modify as needed.