{"categories":[{"label":"Distributed Computing","url":"https://skillfed.io/packages/category/system-distributed-computing/3"}],"enrichment":{"capability":"Provides AI assistants with programmatic access to Apache Airflow via the Model Context Protocol, exposing DAG management, task execution, monitoring, and configuration tools through a FastMCP server.","skillfed_tags":["mcp-server","airflow-integration","ai-assistant"],"use_cases":["Enable Claude or other AI assistants to diagnose and troubleshoot failed DAG runs by fetching task logs and instance details","Automate DAG triggering and monitoring from AI workflows without exposing raw Airflow credentials to the AI system","Provide read-only Airflow visibility to AI assistants for health checks and system overview without write permissions","Integrate Airflow management into IDE-based AI coding assistants (Cursor, VS Code) for workflow development and testing","Query DAG source code, task dependencies, and asset lineage through AI natural-language queries"],"what_it_does":"Astro-airflow-mcp is a Model Context Protocol server that bridges Apache Airflow and AI assistants (Claude, Gemini, Codex, or custom MCP clients). It exposes Airflow's REST API through structured MCP tools, allowing AI systems to query DAG metadata, trigger runs, inspect task logs, manage pools and variables, and diagnose failures\u2014all without direct Airflow access.\n\nThe package runs in two modes: as a standalone HTTP or stdio server, or embedded as an Airflow plugin (2.x Flask or 3.x FastAPI). It automatically detects Airflow version and adapts its API calls accordingly. Authentication supports bearer tokens, username/password with OAuth2 exchange on Airflow 3.x, and custom CA certificates. Core dependencies are fastmcp (the MCP framework), pydantic (validation), httpx (HTTP client), typer (CLI), and utilities like pyyaml and simple-term-menu for configuration and interactive selection.","worth_installing":"Yes, if you run Airflow and want AI assistants to interact with it safely. The package is actively maintained, has no known vulnerabilities, requires only Python 3.10+, and installs with low friction. The unclear license status is a minor concern\u2014verify the actual license before deploying in restricted environments. The MCP abstraction is well-suited for read-only monitoring and safe automation; write operations can be disabled via AF_READ_ONLY."},"id":"astro-airflow-mcp","links":{"html":"https://skillfed.io/packages/astro-airflow-mcp","md":"https://skillfed.io/packages/astro-airflow-mcp.md","pypi":"https://pypi.org/project/astro-airflow-mcp/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-08","license_spdx":null,"license_treatment":"unclear","name":"astro-airflow-mcp","python_support":"supports_current","summary":"A FastMCP server for Airflow integration that can run standalone or as an Airflow 2/3 plugin"},"popularity":{"monthly_downloads":117708,"position":12153,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.9.1"}