airflow-dev-mcp 0.2.0

MCP server for driving a dev/local Airflow cluster over its REST API — trigger DAGs, check run/task status, fetch logs, and diagnose import errors from inside your MCP client.

airflow-dev-mcp

An MCP server that lets an AI coding assistant (Claude Code, Cursor, and other MCP clients) drive a development or local Airflow cluster through Airflow's REST API. It can trigger DAG runs, watch their status, read task logs, and diagnose parse errors.

It talks to Airflow over HTTP only. There's no dependency on your Airflow source tree, no filesystem or database access, and no local config files. All configuration is set through environment variables. It support both Airflow 3 (via /api/v2, the default) and Airflow 2 (/api/v1).

Airflow-dev-mcp is designed for the write-a-DAG / run-it / read-the-logs loop against a development and/or local environment. Pointing it at a production cluster is not recommended.

Install & run

The package ships a single console command, airflow-dev-mcp, which starts the MCP server on stdio. Installation requires uv.

_Note: most users will skip this and just add it to their coding environment (See: below)

To download and validate the package, run:

uvx airflow-dev-mcp --check      # fetch + run a one-shot connectivity check

It can be installed as a persistent tool but typical installation is to just have your coding agent call it through uvx (See: Configure your MCP client below). If you do want to install it system wide, use one of the two following commands

uv tool install airflow-dev-mcp
# or
pipx install airflow-dev-mcp

Configure your MCP client

Claude Code

For most users all you should need to do is add the server to ~/.claude.json (applies everywhere) or a project's .claude/settings.json (just that project):

{
  "mcpServers": {
    "airflow-dev": {
      "command": "uvx",
      "args": ["airflow-dev-mcp"],
      "env": {
        "AIRFLOW_URL": "http://localhost:8080",
        "AIRFLOW_USERNAME": "admin",
        "AIRFLOW_PASSWORD": "admin"
      }
    }
  }
}

Using uvx means you don't have to manage a virtualenv — it fetches and caches the package on first launch. If you'd rather pin an installed copy, replace the command with "command": "airflow-dev-mcp", "args": [] after uv tool install.

Restart Claude Code. The tools show up namespaced as mcp__airflow-dev__trigger_dag, and so on.

Other MCP clients

Any client that launches stdio MCP servers works the same way: run the command airflow-dev-mcp (or uvx airflow-dev-mcp) with the environment variables below.

Configuration

All configuration is via environment variables:

Variable	Default	Description
AIRFLOW_URL	http://localhost:8080	Base URL of the cluster, no path.
AIRFLOW_API_PREFIX	/api/v2	API path prefix. Use /api/v1 for Airflow 2.x.
AIRFLOW_USERNAME	—	Username. Used together with AIRFLOW_PASSWORD.
AIRFLOW_PASSWORD	—	Password.
AIRFLOW_TOKEN	—	Explicit bearer token; skips username/password entirely.
AIRFLOW_AUTH_MODE	auto	auto, jwt, or basic (see below).
AIRFLOW_TOKEN_ENDPOINT	/auth/token	Path used to exchange credentials for a JWT.
AIRFLOW_TIMEOUT	30	HTTP timeout, in seconds.
AIRFLOW_VERIFY_SSL	true	Set false to skip TLS verification (self-signed dev certs).

Authentication

• Airflow 3.x (the default local/MWAA-style image): leave AIRFLOW_AUTH_MODE=auto. The server posts your username/password to /auth/token, caches the returned JWT, and sends it as a bearer token on every request.
• Airflow 2.x: set AIRFLOW_API_PREFIX=/api/v1 and AIRFLOW_AUTH_MODE=basic (2.x uses HTTP basic auth against the REST API).
• Pre-issued token: set AIRFLOW_TOKEN and omit the username/password.

Tools

Tool	What it does
trigger_dag	Start a manual DAG run, optionally with a conf payload. Returns the dag_run_id.
get_run_status	State of a run plus per-task states (task, state, try number, operator, timing).
get_task_logs	Logs for one task attempt, tailed to the last N lines by default.
list_dag_runs	Recent runs of a DAG — find a run when you don't already have its id.
clear_task_instances	Clear tasks so they re-run. Defaults to a dry-run preview.
list_dags	Registered DAGs with their paused / import-error / active flags.
get_import_errors	Parse failures with filename and traceback — why a new DAG isn't showing up.
set_dag_paused	Pause or unpause a DAG (new local DAGs start paused).
list_variables	Read Airflow Variables (read-only).
list_connections	Read Airflow Connections, minus passwords (read-only).

The four list_* tools, get_run_status, get_task_logs, and get_import_errors are strictly read-only. trigger_dag, set_dag_paused, and clear_task_instances change cluster state. There are deliberately no tools that create or modify Variables or Connections.

License

MIT — see LICENSE.