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MCP Apache Airflow 服务管理

提供一种标准化的方式,通过与Apache Airflow 进行标准化交互的方式。

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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

| 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>
AIRFLOW_API_VERSION=v1  # Optional, defaults to v1

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"
      }
    }
  }
}

Alternative configuration 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 following options:

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 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.

The package is deployed automatically to PyPI when project.version is updated in pyproject.toml. Follow semver for versioning.

Please include version update in the PR in order to apply the changes to core logic.

License

MIT License

help

运行方式说明

cloud

托管运行

托管运行通常表示这个 MCP Server 由服务方环境承载,用户一般按页面提供的连接方式或授权流程接入,不需要在本地长期启动一个 MCP 进程

  1. 打开服务方连接页
  2. 完成授权或复制端点
  3. 在 MCP 客户端中连接
terminal

本地运行 / 其它方式

本地运行通常需要用户在自己的电脑或服务器上安装依赖,把 server_config 复制到 MCP 客户端,并按 env_schema 补齐环境变量、密钥或其它配置

  1. 复制 server_config
  2. 安装所需依赖
  3. 补齐环境变量后重启客户端