📌Overview

Purpose: The main objective of this framework is to provide a Model Context Protocol (MCP) server implementation for seamless integration with Apache Airflow and its clients.

Overview: This project encapsulates Apache Airflow's REST API, enabling MCP clients to interact with Airflow in a standardized manner. It leverages the official Apache Airflow client library to ensure optimal compatibility and maintainability.

Key Features:

Comprehensive API Coverage: Supports a wide range of endpoints for managing DAGs, DAG runs, tasks, variables, connections, pools, XComs, and more, allowing for full control over Airflow resources.
Health Monitoring and Configuration Management: Includes endpoints for health checks and configuration retrieval, aiding in effective system monitoring and management.

mcp-server-apache-airflow

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.

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

Feature	API Path	Status
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}`	✅
Clear Task Instances	`/api/v1/dags/{dag_id}/clearTaskInstances`	✅
Set Task Instances State	`/api/v1/dags/{dag_id}/updateTaskInstancesState`	✅
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/{uri}/dagRuns/queued/datasetEvents`	✅
Delete Dataset Queued Events	`/api/v1/datasets/{uri}/dagRuns/queued/datasetEvents`	✅
Monitoring
Get Health	`/api/v1/health`	✅
DAG Stats
Get DAG Stats	`/api/v1/dags/statistics`	✅
Config
Get Config	`/api/v1/config`	✅
Plugins
Get Plugins	`/api/v1/plugins`	✅
Providers
List Providers	`/api/v1/providers`	✅
Event Logs
List Event Logs	`/api/v1/eventLogs`	✅
Get Event Log	`/api/v1/eventLogs/{event_log_id}`	✅
System
Get Import Errors	`/api/v1/importErrors`	✅
Get Import Error Details	`/api/v1/importErrors/{import_error_id}`	✅
Get Health Status	`/api/v1/health`	✅
Get Version	`/api/v1/version`	✅

Setup

Dependencies

This project depends on the official Apache Airflow client library (apache-airflow-client). It will be automatically installed when you install this package.

Environment Variables

Set the following environment variables:

AIRFLOW_HOST=<your-airflow-host>
AIRFLOW_USERNAME=<your-airflow-username>
AIRFLOW_PASSWORD=<your-airflow-password>

Usage with Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "mcp-server-apache-airflow": {
      "command": "uvx",
      "args": ["mcp-server-apache-airflow"],
      "env": {
        "AIRFLOW_HOST": "https://your-airflow-host",
        "AIRFLOW_USERNAME": "your-username",
        "AIRFLOW_PASSWORD": "your-password"
      }
    }
  }
}

Alternatively, using uv:

{
  "mcpServers": {
    "mcp-server-apache-airflow": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/mcp-server-apache-airflow",
        "run",
        "mcp-server-apache-airflow"
      ],
      "env": {
        "AIRFLOW_HOST": "https://your-airflow-host",
        "AIRFLOW_USERNAME": "your-username",
        "AIRFLOW_PASSWORD": "your-password"
      }
    }
  }
}

Replace /path/to/mcp-server-apache-airflow with the actual path where you've cloned the repository.

Selecting the API groups

You can select the API groups you want to use by setting the --apis flag.

uv run mcp-server-apache-airflow --apis "dag,dagrun"

The default is to use all APIs.

Allowed values are:

config
connections
dag
dagrun
dagstats
dataset
eventlog
importerror
monitoring
plugin
pool
provider
taskinstance
variable
xcom

Manual Execution

You can also run the server manually:

make run

make run accepts the following options:

--port: Port to listen on for SSE (default: 8000)
--transport: Transport type (stdio/sse, default: stdio)

Or, you could run the SSE server directly, which accepts the same parameters:

make run-sse

Installing via Smithery

To install Apache Airflow MCP Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @yangkyeongmo/mcp-server-apache-airflow --client claude

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License