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mcp-server-apache-airflow

mcp-server-apache-airflow

A Model Context Protocol server for integrating Apache Airflow with MCP clients.

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mcp-server-apache-airflow provides a Model Context Protocol (MCP) server implementation that allows standardized interaction with Apache Airflow environments. By wrapping Airflow's REST API, it enables MCP clients to manage and orchestrate workflows, DAGs, and runs in a consistent and interoperable manner. This implementation leverages the official Apache Airflow client library to ensure robust compatibility and maintainability. It streamlines the management of Airflow resources by exposing comprehensive endpoint coverage for key workflow operations.

Key Features

Implements Model Context Protocol server for Apache Airflow

Wraps and exposes Airflow REST API endpoints

Supports comprehensive DAG management operations

Enables creation, listing, updating, and deletion of DAG runs

Uses the official Apache Airflow client library

Standardizes workflow interactions for MCP clients

Allows batch operations and DAG file reparsing

Integrates with security and compliance assessment badges

Promotes interoperability across platforms via MCP

Provides detailed endpoint coverage for workflow automation

Use Cases

Standardizing the integration of Apache Airflow with external workflow tools

Allowing MCP clients to automate workflow orchestration in Airflow environments

Facilitating context-aware management of Airflow DAGs and runs

Enabling unified monitoring and updating of complex Airflow-based pipelines

Streamlining workflow automation for organizations using Airflow

Providing a protocol-compliant interface for workflow and job scheduling

Connecting legacy or proprietary workflow orchestrators with Airflow infrastructure

Batch managing and reparsing multiple DAGs programmatically

Supporting secure, auditable access to Airflow process management

Facilitating hybrid cloud or multi-tenant workflow automation scenarios

README

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}`	✅
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:

bash

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:

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"
      }
    }
  }
}

JWT Token Authentication:

json

{
  "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:

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

JWT Token Authentication:

json

{
  "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:

json

{
  "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:

json

{
  "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.

bash

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:

bash

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

Using the environment variable:

bash

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:

bash

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

Manual Execution

You can also run the server manually:

bash

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:

bash

make run-sse

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

bash

uv run src --transport http --port 8080

Installing via Smithery

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

bash

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

Development

Setting up Development Environment

Clone the repository:

bash

git clone https://github.com/yangkyeongmo/mcp-server-apache-airflow.git
cd mcp-server-apache-airflow

Install development dependencies:

bash

uv sync --dev

Create a .env file for environment variables (optional for development):

bash

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:

bash

# Run all tests
make test

Code Quality

bash

# 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.

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

Star History

Repository Owner

yangkyeongmo

User

Repository Details

Language Python

Default Branch main

Size 397 KB

Contributors 9

License MIT License

MCP Verified Nov 12, 2025

Programming Languages

Python

99.35%

Dockerfile

0.38%

Makefile

0.27%

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mcp-server-apache-airflow

Key Features

Use Cases

README

mcp-server-apache-airflow

About

Feature Implementation Status

Setup

Dependencies

Environment Variables

Authentication

Usage with Claude Desktop

Selecting the API groups

Read-Only Mode

Manual Execution

Installing via Smithery

Development

Setting up Development Environment

Running Tests

Code Quality

Continuous Integration

Contributing

License

Star History

Repository Owner

Repository Details

Programming Languages

Tags

Related MCPs