How do I install the mcp airflow simple mcp server?

Use a recorded configuration for your client, replace example credential values and paths, and verify the documented runtime. Restart or reconnect the client, then inspect the available tools.

What credentials does it need?

It reads `GIT_AUTO_UPDATE` from the environment. Store these in your client's env block or a secrets manager — never commit them to a repository — and grant the narrowest scope that still covers your use case.

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MCP Airflow Simple MCP Server

A **Model Context Protocol (MCP) server** for Apache Airflow 3 that provides essential tools for DAG management, monitoring, debugging, and

Local serverstdioPython

What is the MCP Airflow Simple MCP Server?

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

Configuration and credentials

Python 3.8+ - Apache Airflow 3.x with REST API enabled - Network access to Airflow instance

More MCP options

Compare MCP servers by category, or browse hosted MCP servers if you prefer an endpoint over a local process.

Maintainer setup documentation.

Recorded tools

Check the linked publisher documentation and your connected client's tool list. Names and availability can vary with the server version and account permissions.

Tool	What it does
get_dags	List all DAGs in Airflow. json { "only_active": false, "limit": 100 }
get_dag_tasks	Get all tasks in a specific DAG. json { "dag_id": "example_dag" }
trigger_dag_run	Trigger a new DAG run. json { "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). json { "dag_id": "example_dag", "dag_run_id": "manual__2026-01-05T00:00:00+00:00", "dry_run": false }
set_dag_state
get_dag_runs	Get DAG run history with optional state filtering. json { "dag_id": "example_dag", "state": "failed", "limit": 25 }
get_task_instances	Get task instances for a specific DAG run. json { "dag_id": "example_dag", "dag_run_id": "manual__2026-01-05T00:00:00+00:00" }
get_dag_stats
get_task_logs	Get execution logs for a specific task instance. json { "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_connections	List all Airflow connections. json { "limit": 100 }
get_connection	Get details of a specific connection. json { "connection_id": "postgres_default" }
test_connection	Test connection accessibility. json { "connection_id": "postgres_default" }
check_health	Check Airflow system health (includes Metadatabase, Scheduler, Triggerer, and DagProcessor). json {}

How to install the MCP Airflow Simple MCP Server

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

From the project documentation. Replace placeholders and example paths before connecting.

Configuration

Python 3.8+ - Apache Airflow 3.x with REST API enabled - Network access to Airflow instance

Variable	Description	Required
GIT_AUTO_UPDATE	Declared in the maintainer’s client configuration.	Check documentation

Example prompts to try

“Use MCP Airflow Simple to get dags.”
“Use MCP Airflow Simple to get dag tasks.”
“Use MCP Airflow Simple to trigger dag run.”

Frequently asked questions

A **Model Context Protocol (MCP) server** for Apache Airflow 3 that provides essential tools for DAG management, monitoring, debugging, and connection testing Confirm the tools offered by your installed version in the connected client. Tool availability and permissions depend on the server and account configuration.