Manage Airflow DAGs and Runs via MCP
MCP server wrapping Apache Airflow's REST API - manage DAGs, DAG runs, tasks, variables, connections, and pools, with an optional read-only mode.
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
Control DAG lifecycle (pause, resume, delete)
Create, update, and clear DAG runs
Manage Airflow variables and connections
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 all DAGs in the Airflow instance
Get detailed information about a specific DAG
Pause a DAG to prevent new runs from being scheduled
Resume a paused DAG to allow new runs to be scheduled
Update DAG configuration and properties
Delete a DAG from the Airflow instance
Create a new run for a specified DAG
List all runs for a specific DAG
Overview
Airflow MCP Server
An MCP server that wraps Apache Airflow's REST API, giving MCP clients standardized access to DAGs, DAG runs, tasks, variables, connections, pools, datasets, and monitoring endpoints, using Airflow's own official client library. Use it to manage or monitor an Airflow instance from an MCP client. Use its read-only mode or scoped API groups when only inspection, not DAG or connection changes, is wanted.
What it does
An MCP server that wraps Apache Airflow's REST API using Airflow's own official client library, giving any MCP client a standardized way to manage and monitor an Airflow instance. It covers the full surface of Airflow's API: DAGs (list, get, pause, unpause, update, delete, get source, reparse), DAG runs (create, list, get, update, delete, clear, batch-list, set notes), tasks and task instances (list, get details, update state, clear, read logs and retries), variables, connections including connection testing, pools, XComs, datasets and dataset events, plus read endpoints for health, config, plugins, providers, event logs, import errors, and version.
When to use - and when NOT to
Use it when an MCP client needs to inspect or operate an Airflow deployment directly - checking DAG and task status, triggering a run, reading task logs, or managing variables and connections - instead of switching to the Airflow UI. For safety, run in read-only mode, via the READ_ONLY environment variable or the --read-only flag, whenever the client should only inspect state: this exposes listing and get-detail tools plus non-destructive connection testing, but removes every tool that creates, updates, deletes, or triggers a DAG run. Combine read-only mode with the --apis flag to further narrow exposure to just the API groups actually needed, for example dag and variable only, rather than granting write access to Airflow's full surface by default.
Capabilities
Authentication is basic (AIRFLOW_USERNAME and AIRFLOW_PASSWORD) or JWT token (AIRFLOW_JWT_TOKEN, which takes precedence if both are set); a JWT can be obtained by POSTing credentials to Airflow's /auth/token endpoint. AIRFLOW_HOST defaults to http://localhost:8080 and AIRFLOW_API_VERSION defaults to v1. API groups can be selected individually - config, connections, dag, dagrun, dagstats, dataset, eventlog, importerror, monitoring, plugin, pool, provider, taskinstance, variable, xcom - with all groups enabled by default. The server supports stdio, sse, and http transport, selectable via --transport and, for network transports, --port.
How to install
Via Smithery for Claude Desktop:
npx -y @smithery/cli install @yangkyeongmo/mcp-server-apache-airflow --client claude
Or configure Claude Desktop directly:
{
"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"
}
}
}
}
Add READ_ONLY: true to the env block, or run manually with the --read-only flag, to restrict the server to inspection-only tools.
Who it's for
Data and platform engineers who want an AI assistant to inspect or operate Airflow pipelines - checking DAG health, reading task logs, managing variables and connections - without leaving the assistant, and who can scope access down with read-only mode and API-group selection for safety. The project is licensed under MIT.
Source 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:
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
- 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.
FAQ
Common questions
Discussion
Questions & comments · 0
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