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"
      }
    }
  }
}

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.

License

MIT License