mcp-server-apache-airflowThis server connects AI tools directly to Apache Airflow, letting you manage workflows and tasks through conversation. You can view pipeline status, trigger runs, check logs, and update settings witho

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Reviewed by Daniel Nikulshyn·Updated July 2026

Overview

This project implements a Model Context Protocol server for Apache Airflow, enabling seamless integration with MCP clients. It allows MCP clients to interact with Airflow in a standardized way, using the official Apache Airflow client library for compatibility and maintainability. The project provides API paths for DAG management, including listing, getting details, pausing, unpausing, updating, and deleting DAGs. It also includes API paths for DAG runs, such as listing, creating, getting details, updating, and deleting DAG runs. Additionally, the project includes API paths for tasks, variables, connections, pools, XComs, and datasets. These paths enable MCP clients to view pipeline status, trigger runs, check logs, and update settings through conversations. This project provides a standardized way to interact with Apache Airflow, streamlining workflows and making it easier to manage tasks, variables, connections, and datasets through conversation.

Key features

DAG Management
DAG Runs
Task Management
Variables Management
Connections Management
Pools Management

Pricing

Model: Free
Category: MCP Servers
Rating: No reviews yet

Use cases

Workflow Automation

The MCP server enables automation of workflows by allowing MCP clients to interact with Apache Airflow in a standardized way, making it easier to manage complex workflows.

AI Tool Integration

The server allows AI tools to connect directly to Apache Airflow, enabling users to manage workflows and tasks through conversation, viewing pipeline status, triggering runs, checking logs, and updating settings.

Pros & Cons

Pros

Standardized interaction with Apache Airflow
Seamless integration with MCP clients
Compatibility with Airflow's REST API
Maintainability through the official Airflow client library

Cons

Dependent on Apache Airflow and its REST API
Limited to MCP client interactions

Reviews

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Q&A

Is there a learning curve for developers to use this server?

Because it follows Airflow’s standard API conventions and relies on the official client library, developers familiar with Airflow’s REST endpoints will adapt quickly, with minimal extra configuration beyond deploying the MCP server.

Asked by Sofia Lindqvist · Dec 25, 2025

Are there any limitations to using this server?

It is only functional when an Apache Airflow instance and its REST API are available. It also supports interaction exclusively via MCP clients; other non‑MCP tools would need additional wrappers.

Asked by Qiu Yan · Dec 11, 2025

What can I do with the API paths for DAG runs?

The server allows you to list, create, retrieve details, update, and delete DAG runs. You can also trigger runs and view their status directly through MCP client conversations.

Asked by Amara Chukwu · Nov 4, 2025

How does mcp-server-apache-airflow integrate with existing Airflow setups?

It uses the official Apache Airflow client library, exposing REST‑style API paths for DAGs, runs, tasks, variables, connections, pools, XComs, and datasets. Clients can then call these endpoints to manage workflows just as if they were using Airflow’s native REST API.

Asked by Sven Bergqvist · Sep 18, 2025