astro-airflow-mcp
Official> [!WARNING]
> This project has been relocated to the [Astronomer agents monorepo](https://github.com/astronomer/agents/tree/main/astro-airflow-mcp).
---
# Airflow MCP Server
[](https://github.com/astronomer/astro-airflow-mcp/actions/workflows/ci.yml)
[](https://www.python.org/downloads/)
[](https://pypi.org/project/astro-airflow-mcp)
[](https://github.com/astronomer/astro-airflow-mcp/blob/main/LICENSE)
A [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) server for Apache Airflow that provides AI assistants with access to Airflow's REST API. Built with [FastMCP](https://github.com/jlowin/fastmcp).
## Quickstart
### IDEs
<a href="https://insiders.vscode.dev/redirect?url=vscode://ms-vscode.vscode-mcp/install?%7B%22name%22%3A%22astro-airflow-mcp%22%2C%22command%22%3A%22uvx%22%2C%22args%22%3A%5B%22astro-airflow-mcp%22%2C%22--transport%22%2C%22stdio%22%5D%7D"><img src="https://img.shields.io/badge/VS_Code-Install_Server-0098FF?style=flat-square&logo=visualstudiocode&logoColor=white" alt="Install in VS Code" height="32"></a>
<a href="https://cursor.com/en-US/install-mcp?name=astro-airflow-mcp&config=eyJjb21tYW5kIjoidXZ4IiwiYXJncyI6WyJhc3Ryby1haXJmbG93LW1jcCIsIi0tdHJhbnNwb3J0Iiwic3RkaW8iXX0"><img src="https://cursor.com/deeplink/mcp-install-dark.svg" alt="Add to Cursor" height="32"></a>
<details>
<summary>Manual configuration</summary>
Add to your MCP settings (Cursor: `~/.cursor/mcp.json`, VS Code: `.vscode/mcp.json`):
```json
{
"mcpServers": {
"airflow": {
"command": "uvx",
"args": ["astro-airflow-mcp", "--transport", "stdio"]
}
}
}
```
</details>
### CLI Tools
<details>
<summary>Claude Code</summary>
```bash
claude mcp add airflow -- uvx astro-airflow-mcp --transport stdio
```
</details>
<details>
<summary>Gemini CLI</summary>
```bash
gemini mcp add airflow -- uvx astro-airflow-mcp --transport stdio
```
</details>
<details>
<summary>Codex CLI</summary>
```bash
codex mcp add airflow -- uvx astro-airflow-mcp --transport stdio
```
</details>
### Desktop Apps
<details>
<summary>Claude Desktop</summary>
Add to `~/Library/Application Support/Claude/claude_desktop_config.json` (macOS) or `%APPDATA%\Claude\claude_desktop_config.json` (Windows):
```json
{
"mcpServers": {
"airflow": {
"command": "uvx",
"args": ["astro-airflow-mcp", "--transport", "stdio"]
}
}
}
```
</details>
### Other MCP Clients
<details>
<summary>Manual JSON Configuration</summary>
Add to your MCP configuration file:
```json
{
"mcpServers": {
"airflow": {
"command": "uvx",
"args": ["astro-airflow-mcp", "--transport", "stdio"]
}
}
}
```
Or connect to a running HTTP server: `"url": "http://localhost:8000/mcp"`
</details>
> **Note:** No installation required - `uvx` runs directly from PyPI. The `--transport stdio` flag is required because the server defaults to HTTP mode.
### Configuration
By default, the server connects to `http://localhost:8080` (Astro CLI default). Set environment variables for custom Airflow instances:
| Variable | Description |
|----------|-------------|
| `AIRFLOW_API_URL` | Airflow webserver URL |
| `AIRFLOW_USERNAME` | Username (Airflow 3.x uses OAuth2 token exchange) |
| `AIRFLOW_PASSWORD` | Password |
| `AIRFLOW_AUTH_TOKEN` | Bearer token (alternative to username/password) |
Example with auth (Claude Code):
```bash
claude mcp add airflow -e AIRFLOW_API_URL=https://your-airflow.example.com -e AIRFLOW_USERNAME=admin -e AIRFLOW_PASSWORD=admin -- uvx astro-airflow-mcp --transport stdio
```
## Features
- **Airflow 2.x and 3.x Support**: Automatic version detection with adapter pattern
- **MCP Tools** for accessing Airflow data:
- DAG management (list, get details, get source code, stats, warnings, import errors, trigger, pause/unpause)
- Task management (list, get details, get task instances, get logs)
- Pool management (list, get details)
- Variable management (list, get specific variables)
- Connection management (list connections with credentials excluded)
- Asset/Dataset management (unified naming across versions, data lineage)
- Plugin and provider information
- Configuration and version details
- **Consolidated Tools** for agent workflows:
- `explore_dag`: Get comprehensive DAG information in one call
- `diagnose_dag_run`: Debug failed DAG runs with task instance details
- `get_system_health`: System overview with health, errors, and warnings
- **MCP Resources**: Static Airflow info exposed as resources (version, providers, plugins, config)
- **MCP Prompts**: Guided workflows for common tasks (troubleshooting, health checks, onboarding)
- **Dual deployment modes**:
- **Standalone server**: Run as an independent MCP server
- **Airflow plugin**: Integrate directly into Airflow 3.x webserver
- **Flexible Authentication**:
- Bearer token (Airflow 2.x and 3.x)
- Username/password with automatic OAuth2 token exchange (Airflow 3.x)
- Basic auth (Airflow 2.x)
## Available Tools
### Consolidated Tools (Agent-Optimized)
| Tool | Description |
|------|-------------|
| `explore_dag` | Get comprehensive DAG info: metadata, tasks, recent runs, source code |
| `diagnose_dag_run` | Debug a DAG run: run details, failed task instances, logs |
| `get_system_health` | System overview: health status, import errors, warnings, DAG stats |
### Core Tools
| Tool | Description |
|------|-------------|
| `list_dags` | Get all DAGs and their metadata |
| `get_dag_details` | Get detailed info about a specific DAG |
| `get_dag_source` | Get the source code of a DAG |
| `get_dag_stats` | Get DAG run statistics (Airflow 3.x only) |
| `list_dag_warnings` | Get DAG import warnings |
| `list_import_errors` | Get import errors from DAG files that failed to parse |
| `list_dag_runs` | Get DAG run history |
| `get_dag_run` | Get specific DAG run details |
| `trigger_dag` | Trigger a new DAG run (start a workflow execution) |
| `pause_dag` | Pause a DAG to prevent new scheduled runs |
| `unpause_dag` | Unpause a DAG to resume scheduled runs |
| `list_tasks` | Get all tasks in a DAG |
| `get_task` | Get details about a specific task |
| `get_task_instance` | Get task instance execution details |
| `get_task_logs` | Get logs for a specific task instance execution |
| `list_pools` | Get all resource pools |
| `get_pool` | Get details about a specific pool |
| `list_variables` | Get all Airflow variables |
| `get_variable` | Get a specific variable by key |
| `list_connections` | Get all connections (credentials excluded for security) |
| `list_assets` | Get assets/datasets (unified naming across versions) |
| `list_plugins` | Get installed Airflow plugins |
| `list_providers` | Get installed provider packages |
| `get_airflow_config` | Get Airflow configuration |
| `get_airflow_version` | Get Airflow version information |
### MCP Resources
| Resource URI | Description |
|--------------|-------------|
| `airflow://version` | Airflow version information |
| `airflow://providers` | Installed provider packages |
| `airflow://plugins` | Installed Airflow plugins |
| `airflow://config` | Airflow configuration |
### MCP Prompts
| Prompt | Description |
|--------|-------------|
| `troubleshoot_failed_dag` | Guided workflow for diagnosing DAG failures |
| `daily_health_check` | Morning health check routine |
| `onboard_new_dag` | Guide for understanding a new DAG |
## Advanced Usage
### Running as Standalone Server
For HTTP-based integrations or connecting multiple clients to one server:
```bash
# Run server (HTTP mode is default)
uvx astro-airflow-mcp --airflow-url https://my-airflow.example.com --username admin --password admin
```
Connect MCP clients to: `http://localhost:8000/mcp`
### Airflow Plugin Mode
Install into your Airflow 3.x environment to expose MCP at `http://your-airflow:8080/mcp/v1`:
```bash
# Add to your Astro project
echo astro-airflow-mcp >> requirements.txt
```
### CLI Options
| Flag | Environment Variable | Default | Description |
|------|---------------------|---------|-------------|
| `--transport` | `MCP_TRANSPORT` | `stdio` | Transport mode (`stdio` or `http`) |
| `--host` | `MCP_HOST` | `localhost` | Host to bind to (HTTP mode only) |
| `--port` | `MCP_PORT` | `8000` | Port to bind to (HTTP mode only) |
| `--airflow-url` | `AIRFLOW_API_URL` | Auto-discovered or `http://localhost:8080` | Airflow webserver URL |
| `--airflow-project-dir` | `AIRFLOW_PROJECT_DIR` | `$PWD` | Astro project directory for auto-discovering Airflow URL from `.astro/config.yaml` |
| `--auth-token` | `AIRFLOW_AUTH_TOKEN` | `None` | Bearer token for authentication |
| `--username` | `AIRFLOW_USERNAME` | `None` | Username for authentication (Airflow 3.x uses OAuth2 token exchange) |
| `--password` | `AIRFLOW_PASSWORD` | `None` | Password for authentication |
## Architecture
The server is built using [FastMCP](https://github.com/jlowin/fastmcp) with an adapter pattern for Airflow version compatibility:
### Core Components
- **Adapters** (`adapters/`): Version-specific API implementations
- `AirflowAdapter` (base): Abstract interface for all Airflow API operations
- `AirflowV2Adapter`: Airflow 2.x API (`/api/v1`) with basic auth
- `AirflowV3Adapter`: Airflow 3.x API (`/api/v2`) with OAuth2 token exchange
- **Version Detection**: Automatic detection at startup by probing API endpoints
- **Models** (`models.py`): Pydantic models for type-safe API responses
### Version Handling Strategy
1. **Major versions (2.x vs 3.x)**: Adapter pattern with runtime version detection
2. **Minor versions (3.1 vs 3.2)**: Runtime feature detection with graceful fallbacks
3. **New API parameters**: Pass-through `**kwargs` for forward compatibility
### Deployment Modes
- **Standalone**: Independent ASGI application with HTTP/SSE transport
- **Plugin**: Mounted into Airflow 3.x FastAPI webserver
## Development
```bash
# Setup development environment
make install-dev
# Run tests
make test
# Run all checks
make check
# Local testing with Astro CLI
astro dev start # Start Airflow
make run # Run MCP server (connects to localhost:8080)
```
## Contributing
Contributions welcome! Please ensure:
- All tests pass (`make test`)
- Code passes linting (`make check`)
- prek hooks pass (`make prek`)
TDQS
Scored across 31 tools
Each tool targets a distinct aspect of Airflow (DAGs, runs, tasks, instances, logs, pools, variables, etc.), with clear descriptions that prevent confusion. Even similar tools like 'explore_dag' and 'get_dag_details' serve different granularity levels.
Most tools follow a verb_noun pattern (list_dags, get_dag_details, trigger_dag), but a few deviate (diagnose_dag_run, explore_dag, trigger_dag_and_wait). Overall, the pattern is clear and predictable.
31 tools is high but appropriate for a comprehensive Airflow MCP server covering many subsystems. Each tool has a clear purpose, though some consolidation (e.g., get_dag_details and explore_dag) could reduce count slightly.
The tool set is strong for monitoring and troubleshooting (DAGs, runs, tasks, logs, system health) but misses write operations for variables, connections, and pools. This leaves agents without the ability to create or update these resources.