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MCP Server for Apache Airflow

by yangkyeongmo
GitHub
Python
MIT License
77
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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

FeatureAPI PathStatus
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> # Optional, defaults to http://localhost:8080 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" } } } }

For read-only mode (recommended for safety):

{ "mcpServers": { "mcp-server-apache-airflow": { "command": "uvx", "args": ["mcp-server-apache-airflow", "--read-only"], "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 --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. This will only expose tools that perform read operations (GET requests) and exclude any tools that create, update, or delete resources.

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

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, 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

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.

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

Install Server
A
security – no known vulnerabilities
A
license - permissive license
A
quality - confirmed to work

How are these scores calculated?

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Tools

View all tools

Bietet MCP-Clients eine standardisierte Möglichkeit zur Interaktion mit der REST-API von Apache Airflow und unterstützt Vorgänge wie die DAG-Verwaltung und die Überwachung der Systemintegrität von Airflow.

  1. Um
    1. Status der Funktionsimplementierung
      1. Aufstellen
        1. Abhängigkeiten
        2. Umgebungsvariablen
        3. Verwendung mit Claude Desktop
        4. Auswählen der API-Gruppen
        5. Manuelle Ausführung
        6. Installation über Smithery
      2. Beitragen
        1. Lizenz

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