Mcp Server Apache Airflow
by
Provides a Model Context Protocol server that wraps Apache Airflow's REST API, enabling MCP clients to manage DAGs, runs, tasks, variables, connections, and more through standardized endpoints.
Mcp Server Apache Airflow Overview
What is Mcp Server Apache Airflow about?
Mcp Server Apache Airflow offers a ready‑to‑use MCP server implementation that proxies Apache Airflow’s REST API. By exposing Airflow functionality through MCP tools, clients can interact with DAG management, execution, monitoring, and configuration without dealing directly with Airflow’s native endpoints.
How to use Mcp Server Apache Airflow?
- Install the package (e.g., via
uvormake). - Configure environment variables for the Airflow host and credentials:
AIRFLOW_HOST=https://your-airflow-host AIRFLOW_USERNAME=your-username AIRFLOW_PASSWORD=your-password - Run the server:
- Default (all APIs):
uv run mcp-server-apache-airflow - Read‑only mode:
uv run mcp-server-apache-airflow --read-only - Select specific API groups:
uv run mcp-server-apache-airflow --apis dag --apis variable - SSE transport:
make run-sse -- --port 8000
- Default (all APIs):
- Integrate with Claude Desktop or other MCP clients by adding the command and environment to
claude_desktop_config.json.
Key features of Mcp Server Apache Airflow
- Full coverage of Airflow REST endpoints (DAGs, DAG runs, tasks, variables, connections, pools, XComs, datasets, monitoring, plugins, providers, event logs, import errors, health, version, etc.)
- Individual feature status shown as ✅ (all implemented)
- Ability to limit exposed APIs with
--apisflag - Read‑only mode that restricts to GET operations for safe usage
- Multiple transport options (STDIO, Server‑Sent Events)
- Easy installation via
uv,make, or Smithery CLI - CI pipeline validates across Python 3.10‑3.12 with linting and testing
Use cases of Mcp Server Apache Airflow
- Enterprise workflow orchestration: Allow non‑technical tools or LLM agents to trigger, monitor, and manage Airflow DAGs without direct API knowledge.
- LLM‑driven automation: Enable Claude Desktop or other AI assistants to query DAG status, list runs, or fetch logs via natural‑language commands.
- Read‑only monitoring dashboards: Expose Airflow health and metrics safely to external monitoring platforms.
- Controlled automation environments: Deploy the server in read‑only mode for auditing or compliance checks while preventing modifications.
- Custom integration layer: Wrap Airflow in a unified MCP interface alongside other services (e.g., databases, messaging) for a consistent automation stack.
FAQ from the Mcp Server Apache Airflow
Q: Do I need to install the Apache Airflow client library manually?
A: No. The apache-airflow-client dependency is declared in the package and installed automatically.
Q: How can I run the server on a different port?
A: Use the --port option when invoking make run or the SSE server, e.g., make run -- --port 9000.
Q: What if I only need a subset of the APIs?
A: Specify the desired groups with repeated --apis flags, such as --apis dag --apis variable.
Q: Is there a way to test connections without affecting production?
A: Yes. The test connection endpoint is read‑only and safe to call even in production environments.
Q: How is versioning handled?
A: The project follows semantic versioning in pyproject.toml; updating project.version triggers an automatic PyPI release.
Q: Can I contribute new features? A: Contributions are welcomed via pull requests. Ensure the version is bumped appropriately and all CI checks pass.
Mcp Server Apache Airflow's 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} |
✅ |
| 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
- Clone the repository:
git clone https://github.com/yangkyeongmo/mcp-server-apache-airflow.git
cd mcp-server-apache-airflow
- Install development dependencies:
uv sync --dev
- Create a
.envfile for environment variables (optional for development):
touch .env
Note: No environment variables are required for running tests. The
AIRFLOW_HOSTdefaults tohttp://localhost:8080for 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
mainbranch
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
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Mcp Server Apache Airflow's Information
- Author
- yangkyeongmo
- Category
- Workflow & Automation
- Language
- Python
- License
- MIT License
- Stars
- 77
- Updated
- 21 days ago
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