astro-airflow-mcp▌
by astronomer
astro-airflow-mcp: AI assistant access to Apache Airflow REST API for DAG management, task monitoring, logs, and diagnos
An MCP server that enables AI assistants to interact with Apache Airflow's REST API for DAG management, task monitoring, and system diagnostics. It provides comprehensive tools for triggering workflows, retrieving logs, and inspecting system health across Airflow 2.x and 3.x versions.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / Data engineers managing Airflow pipelines
- / DevOps teams monitoring workflow systems
- / Debugging failed DAG runs
- / Airflow system administration
capabilities
- / Trigger DAG runs
- / Retrieve task and workflow logs
- / Monitor DAG and task statuses
- / Check system health
- / List and inspect DAGs
- / Query workflow execution history
what it does
Connects AI assistants to Apache Airflow's REST API to manage workflows, monitor tasks, and diagnose system issues. Provides comprehensive Airflow operations through conversational interface.
about
astro-airflow-mcp is an official MCP server published by astronomer that provides AI assistants with tools and capabilities via the Model Context Protocol. astro-airflow-mcp: AI assistant access to Apache Airflow REST API for DAG management, task monitoring, logs, and diagnos It is categorized under developer tools.
how to install
You can install astro-airflow-mcp in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
license
Apache-2.0
astro-airflow-mcp is released under the Apache-2.0 license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
readme
[!WARNING] This project has been relocated to the Astronomer agents monorepo.
Airflow MCP Server
A Model Context Protocol (MCP) server for Apache Airflow that provides AI assistants with access to Airflow's REST API. Built with 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):
{
"mcpServers": {
"airflow": {
"command": "uvx",
"args": ["astro-airflow-mcp", "--transport", "stdio"]
}
}
}
</details>
CLI Tools
<details> <summary>Claude Code</summary>claude mcp add airflow -- uvx astro-airflow-mcp --transport stdio
</details>
<details>
<summary>Gemini CLI</summary>
gemini mcp add airflow -- uvx astro-airflow-mcp --transport stdio
</details>
<details>
<summary>Codex CLI</summary>
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):
{
"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:
{
"mcpServers": {
"airflow": {
"command": "uvx",
"args": ["astro-airflow-mcp", "--transport", "stdio"]
}
}
}
Or connect to a running HTTP server: "url": "http://localhost:8000/mcp"
Note: No installation required -
uvxruns directly from PyPI. The--transport stdioflag 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):
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 calldiagnose_dag_run: Debug failed DAG runs with task instance detailsget_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:
# 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:
# 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 with an adapter pattern for Airflow version compatibility:
Core Components
- Adapters (
adapters/): Version-specific API implementationsAirflowAdapter(base): Abstract interface for all Airflow API operationsAirflowV2Adapter: Airflow 2.x API (/api/v1) with basic authAirflowV3Adapter: 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
- Major versions (2.x vs 3.x): Adapter pattern with runtime version detection
- Minor versions (3.1 vs 3.2): Runtime feature detection with graceful fallbacks
- New API parameters: Pass-th
FAQ
- What is the astro-airflow-mcp MCP server?
- astro-airflow-mcp is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for astro-airflow-mcp?
- This profile displays 25 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.4 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Extended AI Capabilities
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Context Enhancement
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Workflow Automation
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
- ›Basic understanding of MCP architecture and capabilities
- ›Access credentials for integrated services (if required)
- ›Willingness to experiment and iterate on configuration
Time Estimate
15-60 minutes depending on server complexity
Installation Steps
- 1.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 7.Document successful patterns for reuse
Troubleshooting
- ⚠MCP server not loading: Check config syntax, verify installation
- ⚠Connection errors: Check network, firewall, credentials
- ⚠Feature not working: Read server docs, check required parameters
- ⚠Performance issues: Monitor resource usage, check for network latency
- ⚠Conflicts with other servers: Check port assignments, namespace collisions
Best Practices▌
✓ Do
- +Read server documentation thoroughly before setup
- +Start with simple use cases to validate functionality
- +Test in non-production environment first
- +Monitor resource usage and performance
- +Keep servers updated for bug fixes and new features
- +Document configuration for team members
- +Use environment variables for sensitive configuration
✗ Don't
- −Don't grant overly permissive access to MCP servers
- −Don't skip reading security considerations in docs
- −Don't expose sensitive data without proper controls
- −Don't run untrusted MCP servers without code review
- −Don't ignore error messages—investigate root cause
💡 Pro Tips
- ★Combine multiple MCP servers for powerful workflows
- ★Create custom MCP servers for your specific needs
- ★Share successful configurations with team
- ★Use MCP inspector for debugging
- ★Join MCP community for tips and troubleshooting
Technical Details▌
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
- Model Context Protocol (MCP)
- JSON-RPC 2.0
- stdio or HTTP transport
Compatibility
- Claude Desktop
- Cursor IDE
- Custom MCP clients
When to Use This▌
✓ Use When
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid When
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.
Integration▌
- →Tool composition: Chain multiple MCP tools in workflows
- →Context augmentation: Provide AI with relevant external data
- →Action delegation: Let AI execute tasks on external systems
- →Bidirectional sync: Keep AI context and external systems in sync
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
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Ratings
4.4★★★★★25 reviews- ★★★★★Ava Ghosh· Dec 20, 2024
astro-airflow-mcp is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Rahul Santra· Nov 11, 2024
I recommend astro-airflow-mcp for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Isabella White· Nov 11, 2024
astro-airflow-mcp reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Pratham Ware· Oct 2, 2024
Strong directory entry: astro-airflow-mcp surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Isabella Srinivasan· Oct 2, 2024
Useful MCP listing: astro-airflow-mcp is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Neel Chawla· Sep 25, 2024
astro-airflow-mcp is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Yash Thakker· Sep 17, 2024
astro-airflow-mcp is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Neel White· Aug 16, 2024
We evaluated astro-airflow-mcp against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Dhruvi Jain· Aug 8, 2024
We evaluated astro-airflow-mcp against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Oshnikdeep· Jul 27, 2024
Useful MCP listing: astro-airflow-mcp is the kind of server we cite when onboarding engineers to host + tool permissions.
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