developer-tools

astro-airflow-mcp

astronomer

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.

github stars

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Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Supports Airflow 2.x and 3.xBuilt by AstronomerOne-click IDE installation

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

CI Python 3.10+ PyPI - Version License: Apache 2.0

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"

</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:

VariableDescription
AIRFLOW_API_URLAirflow webserver URL
AIRFLOW_USERNAMEUsername (Airflow 3.x uses OAuth2 token exchange)
AIRFLOW_PASSWORDPassword
AIRFLOW_AUTH_TOKENBearer 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 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)

ToolDescription
explore_dagGet comprehensive DAG info: metadata, tasks, recent runs, source code
diagnose_dag_runDebug a DAG run: run details, failed task instances, logs
get_system_healthSystem overview: health status, import errors, warnings, DAG stats

Core Tools

ToolDescription
list_dagsGet all DAGs and their metadata
get_dag_detailsGet detailed info about a specific DAG
get_dag_sourceGet the source code of a DAG
get_dag_statsGet DAG run statistics (Airflow 3.x only)
list_dag_warningsGet DAG import warnings
list_import_errorsGet import errors from DAG files that failed to parse
list_dag_runsGet DAG run history
get_dag_runGet specific DAG run details
trigger_dagTrigger a new DAG run (start a workflow execution)
pause_dagPause a DAG to prevent new scheduled runs
unpause_dagUnpause a DAG to resume scheduled runs
list_tasksGet all tasks in a DAG
get_taskGet details about a specific task
get_task_instanceGet task instance execution details
get_task_logsGet logs for a specific task instance execution
list_poolsGet all resource pools
get_poolGet details about a specific pool
list_variablesGet all Airflow variables
get_variableGet a specific variable by key
list_connectionsGet all connections (credentials excluded for security)
list_assetsGet assets/datasets (unified naming across versions)
list_pluginsGet installed Airflow plugins
list_providersGet installed provider packages
get_airflow_configGet Airflow configuration
get_airflow_versionGet Airflow version information

MCP Resources

Resource URIDescription
airflow://versionAirflow version information
airflow://providersInstalled provider packages
airflow://pluginsInstalled Airflow plugins
airflow://configAirflow configuration

MCP Prompts

PromptDescription
troubleshoot_failed_dagGuided workflow for diagnosing DAG failures
daily_health_checkMorning health check routine
onboard_new_dagGuide 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

FlagEnvironment VariableDefaultDescription
--transportMCP_TRANSPORTstdioTransport mode (stdio or http)
--hostMCP_HOSTlocalhostHost to bind to (HTTP mode only)
--portMCP_PORT8000Port to bind to (HTTP mode only)
--airflow-urlAIRFLOW_API_URLAuto-discovered or http://localhost:8080Airflow webserver URL
--airflow-project-dirAIRFLOW_PROJECT_DIR$PWDAstro project directory for auto-discovering Airflow URL from .astro/config.yaml
--auth-tokenAIRFLOW_AUTH_TOKENNoneBearer token for authentication
--usernameAIRFLOW_USERNAMENoneUsername for authentication (Airflow 3.x uses OAuth2 token exchange)
--passwordAIRFLOW_PASSWORDNonePassword for authentication

Architecture

The server is built using 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-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. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 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

4.425 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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