Apache Airflow

Apache Airflow

yangkyeongmo

Connects to Apache Airflow clusters via REST API to let you manage workflows, monitor tasks, and access performance data using natural language commands instead of complex API calls.

Provides a bridge to Apache Airflow for managing and monitoring workflows through natural language, enabling DAG management, task execution, and resource administration without leaving your assistant interface.

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What it does

  • Manage DAG operations and lifecycle
  • Monitor task execution and status
  • Access XCom data between tasks
  • Control connection pools and variables
  • Track performance analytics and logs
  • Handle import errors and debugging

Best for

Data engineers managing Airflow workflowsDevOps teams monitoring pipeline healthAnalysts accessing workflow performance dataTeams wanting natural language Airflow control
Natural language workflow managementSupports Airflow API v1 and v2Complete REST API coverage

About Apache Airflow

Apache Airflow is a community-built MCP server published by yangkyeongmo that provides AI assistants with tools and capabilities via the Model Context Protocol. Manage and monitor workflows using Apache Airflow. Streamline workflow automation software and enable automated approval in your assistant interface.

How to install

You can install Apache Airflow in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

License

Apache Airflow is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

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mcp-server-apache-airflow

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A Model Context Protocol (MCP) server implementation for Apache Airflow, enabling seamless integration with MCP clients. This project provides a standardized way to interact with Apache Airflow through the Model Context Protocol.

Server for Apache Airflow MCP server

About

This project implements a Model Context Protocol server that wraps Apache Airflow's REST API, allowing MCP clients to interact with Airflow in a standardized way. It uses the official Apache Airflow client library to ensure compatibility and maintainability.

Feature Implementation Status

FeatureAPI PathStatus
DAG Management
List DAGs/api/v1/dags✅
Get DAG Details/api/v1/dags/{dag_id}✅
Pause DAG/api/v1/dags/{dag_id}✅
Unpause DAG/api/v1/dags/{dag_id}✅
Update DAG/api/v1/dags/{dag_id}✅
Delete DAG/api/v1/dags/{dag_id}✅
Get DAG Source/api/v1/dagSources/{file_token}✅
Patch Multiple DAGs/api/v1/dags✅
Reparse DAG File/api/v1/dagSources/{file_token}/reparse✅
DAG Runs
List DAG Runs/api/v1/dags/{dag_id}/dagRuns✅
Create DAG Run/api/v1/dags/{dag_id}/dagRuns✅
Get DAG Run Details/api/v1/dags/{dag_id}/dagRuns/{dag_run_id}✅
Update DAG Run/api/v1/dags/{dag_id}/dagRuns/{dag_run_id}✅
Delete DAG Run/api/v1/dags/{dag_id}/dagRuns/{dag_run_id}✅
Get DAG Runs Batch/api/v1/dags/~/dagRuns/list✅
Clear DAG Run/api/v1/dags/{dag_id}/dagRuns/{dag_run_id}/clear✅
Set DAG Run Note/api/v1/dags/{dag_id}/dagRuns/{dag_run_id}/setNote✅
Get Upstream Dataset Events/api/v1/dags/{dag_id}/dagRuns/{dag_run_id}/upstreamDatasetEvents✅
Tasks
List DAG Tasks/api/v1/dags/{dag_id}/tasks✅
Get Task Details/api/v1/dags/{dag_id}/tasks/{task_id}✅
Get Task Instance/api/v1/dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances/{task_id}✅
List Task Instances/api/v1/dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances✅
Update Task Instance/api/v1/dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances/{task_id}✅
Get Task Instance Log/api/v1/dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances/{task_id}/logs/{task_try_number}✅
Clear Task Instances/api/v1/dags/{dag_id}/clearTaskInstances✅
Set Task Instances State/api/v1/dags/{dag_id}/updateTaskInstancesState✅
List Task Instance Tries/api/v1/dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances/{task_id}/tries✅
Variables
List Variables/api/v1/variables✅
Create Variable/api/v1/variables✅
Get Variable/api/v1/variables/{variable_key}✅
Update Variable/api/v1/variables/{variable_key}✅
Delete Variable/api/v1/variables/{variable_key}✅
Connections
List Connections/api/v1/connections✅
Create Connection/api/v1/connections✅
Get Connection/api/v1/connections/{connection_id}✅
Update Connection/api/v1/connections/{connection_id}✅
Delete Connection/api/v1/connections/{connection_id}✅
Test Connection/api/v1/connections/test✅
Pools
List Pools/api/v1/pools✅
Create Pool/api/v1/pools✅
Get Pool/api/v1/pools/{pool_name}✅
Update Pool/api/v1/pools/{pool_name}✅
Delete Pool/api/v1/pools/{pool_name}✅
XComs
List XComs/api/v1/dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances/{task_id}/xcomEntries✅
Get XCom Entry/api/v1/dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances/{task_id}/xcomEntries/{xcom_key}✅
Datasets
List Datasets/api/v1/datasets✅
Get Dataset/api/v1/datasets/{uri}✅
Get Dataset Events/api/v1/datasetEvents✅
Create Dataset Event/api/v1/datasetEvents✅
Get DAG Dataset Queued Event/api/v1/dags/{dag_id}/dagRuns/queued/datasetEvents/{uri}✅
Get DAG Dataset Queued Events/api/v1/dags/{dag_id}/dagRuns/queued/datasetEvents✅
Delete DAG Dataset Queued Event/api/v1/dags/{dag_id}/dagRuns/queued/datasetEvents/{uri}✅
Delete DAG Dataset Queued Events/api/v1/dags/{dag_id}/dagRuns/queued/datasetEvents✅
Get Dataset Queued Events`/api/v1/datasets/

README truncated. View full README on GitHub.

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