MCP Connector

Manage Airflow DAGs and Runs via MCP

Airflow MCP Server integrates Apache Airflow with MCP clients via the Model Context Protocol, enabling standardized data pipeline management.

Works with airflow

91
Spark score
out of 100
Updated 4 months ago
Version 0.2.10
Models
universal

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Why it matters

Standardize interaction with Apache Airflow's REST API using the Model Context Protocol. Enables clients to manage DAGs, runs, tasks, variables, and connections programmatically.

Outcomes

What it gets done

01

Control DAG lifecycle (pause, resume, delete)

02

Create, update, and clear DAG runs

03

Manage Airflow variables and connections

04

Access task logs and XCom data

Install

Add it to your toolbox

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/vb-airflow | bash

Capabilities

Tools your agent gets

list_dags

List all DAGs in the Airflow instance

get_dag_details

Get detailed information about a specific DAG

pause_dag

Pause a DAG to prevent new runs from being scheduled

resume_dag

Resume a paused DAG to allow new runs to be scheduled

update_dag

Update DAG configuration and properties

delete_dag

Delete a DAG from the Airflow instance

create_dag_run

Create a new run for a specified DAG

list_dag_runs

List all runs for a specific DAG

+12 tools

Overview

Airflow MCP Server

This asset provides an implementation of the Model Context Protocol (MCP) server for Apache Airflow. It allows MCP clients to interact with Airflow's REST API in a standardized manner. Use this asset when you need to integrate Apache Airflow with MCP clients, enabling standardized access to Airflow's features for managing DAGs, DAG runs, tasks, variables, connections, and pools.

What it does

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 provides a standardized interface for MCP clients to interact with Apache Airflow's features, such as managing DAGs, DAG runs, tasks, variables, connections, and pools.

Here's an example of how to configure it:

{
  "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"
      }
    }
  }
}
Source README

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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}
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/{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>        # Optional, defaults to http://localhost:8080
AIRFLOW_API_VERSION=v1                  # Optional, defaults to v1
READ_ONLY=true                          # Optional, enables read-only mode (true/false, defaults to false)
Authentication

Choose one of the following authentication methods:

Basic Authentication (default):

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

JWT Token Authentication:

AIRFLOW_JWT_TOKEN=<your-jwt-token>

To obtain a JWT token, you can use Airflow's authentication endpoint:

ENDPOINT_URL="http://localhost:8080"  # Replace with your Airflow endpoint
curl -X 'POST' \
  "${ENDPOINT_URL}/auth/token" \
  -H 'Content-Type: application/json' \
  -d '{ "username": "<your-username>", "password": "<your-password>" }'

Note: If both JWT token and basic authentication credentials are provided, JWT token takes precedence.

Usage with Claude Desktop

Add to your claude_desktop_config.json:

Basic Authentication:

{
  "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"
      }
    }
  }
}

JWT Token Authentication:

{
  "mcpServers": {
    "mcp-server-apache-airflow": {
      "command": "uvx",
      "args": ["mcp-server-apache-airflow"],
      "env": {
        "AIRFLOW_HOST": "https://your-airflow-host",
        "AIRFLOW_JWT_TOKEN": "your-jwt-token"
      }
    }
  }
}

For read-only mode (recommended for safety):

Basic Authentication:

{
  "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",
        "READ_ONLY": "true"
      }
    }
  }
}

JWT Token Authentication:

{
  "mcpServers": {
    "mcp-server-apache-airflow": {
      "command": "uvx",
      "args": ["mcp-server-apache-airflow", "--read-only"],
      "env": {
        "AIRFLOW_HOST": "https://your-airflow-host",
        "AIRFLOW_JWT_TOKEN": "your-jwt-token"
      }
    }
  }
}

Alternative configuration using uv:

Basic Authentication:

{
  "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"
      }
    }
  }
}

JWT Token Authentication:

{
  "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_JWT_TOKEN": "your-jwt-token"
      }
    }
  }
}

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 --apis 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

Read-Only Mode

You can run the server in read-only mode by using the --read-only flag or by setting the READ_ONLY=true environment variable. This will only expose tools that perform read operations (GET requests) and exclude any tools that create, update, or delete resources.

Using the command-line flag:

uv run mcp-server-apache-airflow --read-only

Using the environment variable:

READ_ONLY=true uv run mcp-server-apache-airflow

In read-only mode, the server will only expose tools like:

  • Listing DAGs, DAG runs, tasks, variables, connections, etc.
  • Getting details of specific resources
  • Reading configurations and monitoring information
  • Testing connections (non-destructive)

Write operations like creating, updating, deleting DAGs, variables, connections, triggering DAG runs, etc. will not be available in read-only mode.

You can combine read-only mode with API group selection:

uv run mcp-server-apache-airflow --read-only --apis dag --apis variable

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/http, default: stdio)

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

make run-sse

Also, you could start service directly using uv like in the following command:

uv run src --transport http --port 8080

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

Development

Setting up Development Environment

  1. Clone the repository:
git clone https://github.com/yangkyeongmo/mcp-server-apache-airflow.git
cd mcp-server-apache-airflow
  1. Install development dependencies:
uv sync --dev
  1. Create a .env file for environment variables (optional for development):
touch .env

Note: No environment variables are required for running tests. The AIRFLOW_HOST defaults to http://localhost:8080 for development and testing purposes.

Running Tests

The project uses pytest for testing with the following commands available:

# Run all tests
make test

Code Quality

# Run linting
make lint

# Run code formatting
make format

Continuous Integration

The project includes a GitHub Actions workflow (.github/workflows/test.yml) that automatically:

  • Runs tests on Python 3.10, 3.11, and 3.12
  • Executes linting checks using ruff
  • Runs on every push and pull request to main branch

The CI pipeline ensures code quality and compatibility across supported Python versions before any changes are merged.

FAQ

Common questions

Discussion

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