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mcp-server-apache-airflow

MCP.Pizza Chef: yangkyeongmo

mcp-server-apache-airflow is an MCP server that wraps Apache Airflow's REST API, enabling standardized interaction with Airflow through MCP clients. It uses the official Apache Airflow client library to ensure compatibility and maintainability, allowing developers to integrate Airflow workflows into AI-enhanced environments and automate complex data pipelines with real-time model context.

Use This MCP server To

Integrate Apache Airflow workflows with MCP-enabled AI agents Trigger and monitor Airflow DAG runs via MCP clients Fetch Airflow task and DAG status for real-time insights Automate data pipeline management using LLMs and Airflow API Standardize Airflow API access across multiple MCP clients Enable AI-driven scheduling and orchestration of workflows

README

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.

License

MIT License

mcp-server-apache-airflow FAQ

How does mcp-server-apache-airflow connect to Apache Airflow?
It wraps the official Apache Airflow REST API using the official client library to ensure reliable and compatible communication.
Can I trigger Airflow DAG runs through this MCP server?
Yes, the server exposes endpoints to start, monitor, and manage DAG runs via MCP clients.
Is this MCP server compatible with all versions of Apache Airflow?
It is designed to work with Apache Airflow versions supported by the official client library, ensuring broad compatibility.
How does this server handle authentication with Airflow?
Authentication is managed through the underlying Airflow client library, supporting standard Airflow authentication methods like basic auth and token-based auth.
Can this MCP server provide real-time status updates of Airflow tasks?
Yes, it can fetch and relay task and DAG run statuses to MCP clients for real-time monitoring.
Does this server support custom Airflow operators or plugins?
It primarily interacts with Airflow's REST API, so custom operators are accessible if exposed via the API.
What LLM providers can be used with this MCP server?
It is provider-agnostic and works with OpenAI, Anthropic Claude, and Google Gemini models through MCP clients.
How do I deploy mcp-server-apache-airflow?
Deployment involves running the server alongside your Airflow instance, configured to connect via the Airflow REST API endpoint.