astro-airflow-mcp
A FastMCP server for Airflow integration that can run standalone or as an Airflow 2/3 plugin
Decision gist · record as of 2026-08-14
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—verify 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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Airflow instance must be running and accessible at the configured AIRFLOW_API_URL (defaults to http://localhost:8080).
- Low friction install with a pure-Python wheel and eight runtime dependencies including fastmcp, pydantic, and httpx.
License · maintenance · safety
(unclear)
last release 2026-07-08 (37 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 117,708 downloads/mo, #12,153 on PyPI
Alternatives
Verify before relying
pip install astro-airflow-mcp
# Configure for Claude Desktop
# Add to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"airflow": {
"command": "uvx",
"args": ["astro-airflow-mcp", "--transport", "stdio"]
}
}
}
# Or run standalone with custom Airflow instance:
export AIRFLOW_API_URL=http://localhost:8080
export AIRFLOW_USERNAME=admin
export AIRFLOW_PASSWORD=admin
uvx astro-airflow-mcp --transport stdio- Whether the package works with all Airflow 2.x and 3.x versions or only specific minor releases
- Performance characteristics when managing large numbers of DAGs or task instances
- Whether MCP resource and prompt features are fully documented and stable
What it is and 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—all without direct Airflow access.
The 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.
Use it for
- 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
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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—verify 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.
Install
astro-airflow-mcp on PyPI
Before you install
Low friction install with a pure-Python wheel and eight runtime dependencies including fastmcp, pydantic, and httpx. Actively maintained with a release 37 days ago. Requires Python 3.10 or later.
Requires Python 3.10 or later. Airflow instance must be running and accessible at the configured AIRFLOW_API_URL (defaults to http://localhost:8080).
Quickstart
pip install astro-airflow-mcp
# Configure for Claude Desktop
# Add to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"airflow": {
"command": "uvx",
"args": ["astro-airflow-mcp", "--transport", "stdio"]
}
}
}
# Or run standalone with custom Airflow instance:
export AIRFLOW_API_URL=http://localhost:8080
export AIRFLOW_USERNAME=admin
export AIRFLOW_PASSWORD=admin
uvx astro-airflow-mcp --transport stdioVerify before relying
- Whether the package works with all Airflow 2.x and 3.x versions or only specific minor releases
- Performance characteristics when managing large numbers of DAGs or task instances
- Whether MCP resource and prompt features are fully documented and stable
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 8 packagesfastmcpfilelockhttpxpackagingpydanticpyyamlsimple-term-menutyper |
| Maintenance | Actively maintained 37 days since the last release |
| First released | |
| Downloads | 117,708 / month, #12,153 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: astro_airflow_mcp-0.9.1-py3-none-any.whl
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See also airflow-mcd · apache-airflow-core · tilt-mcp · astronomer-starship · mfa-servicenow-mcp · godot-ai · nextcloud-mcp-server · airbyte-internal-ops · ha-mcp · arcade-mcp-server