This server lets an AI coding assistant drive a development or local Apache Airflow cluster through Airflow's REST API, enabling the full write-a-DAG / run-it / read-the-logs development loop.
DAG Management
list_dags — List registered DAGs with their paused/active/import-error flags, file locations, tags, and scheduling info; filter by pattern or tags
set_dag_paused — Pause or unpause a DAG (new local DAGs start paused by default, blocking execution)
get_import_errors — Retrieve DAG parse failures with filenames and full tracebacks — the primary tool for diagnosing why a new DAG isn't showing up
DAG Run Operations
trigger_dag — Manually start a DAG run, optionally with a conf payload, logical date, and a note; returns the dag_run_id
list_dag_runs — List recent runs for a given DAG or across all DAGs, filterable by state
get_run_status — Get the state of a DAG run plus per-task states (task ID, state, try number, operator, timing, duration)
Task Operations
get_task_logs — Fetch logs for a specific task instance attempt, with try_number, map_index, and tail_lines options
clear_task_instances — Clear task instances for re-execution after a fix; supports dry-run preview, scoped clearing, and resetting DAG run state
Configuration Inspection (Read-Only)
list_variables — Read Airflow Variables (sensitive values masked; no create/modify)
list_connections — Read Airflow Connection metadata (passwords never returned; no create/modify)
Supports both Airflow 3 (default) and Airflow 2 via configurable API prefix and auth mode.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@airflow-dev-mcp Trigger the my_etl DAG and check its status"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
This is an MCP server for driving a local or development Airflow cluster from an AI coding assistant such as Claude Code, Cursor, or any other MCP client. Use it to trigger DAG runs, watch their status, read task logs, and see why a DAG failed to parse, all without leaving your editor.
The server talks to Airflow through its REST API and nothing else. It never touches your
DAG source, filesystem, or database, and there are no config files to manage; everything
is set with environment variables. Both Airflow 3 (/api/v2) and Airflow 2 (/api/v1)
work, and the version is detected automatically.
The quickest way is claude mcp add, run from your project directory:
claude mcp add airflow-dev \
-e AIRFLOW_URL=http://localhost:8080 \
-e AIRFLOW_USERNAME=admin \
-e AIRFLOW_PASSWORD=admin \
-- uvx airflow-dev-mcpAdd --scope user to make it available in every project, or --scope project to write a
.mcp.json you can commit for your team. The default scope is local to you in the current
project.
To set it up by hand instead, put the following in a .mcp.json file at your project root:
{
"mcpServers": {
"airflow-dev": {
"command": "uvx",
"args": ["airflow-dev-mcp"],
"env": {
"AIRFLOW_URL": "http://localhost:8080",
"AIRFLOW_USERNAME": "admin",
"AIRFLOW_PASSWORD": "admin"
}
}
}
}For a setup that applies everywhere, put the same mcpServers block in ~/.claude.json.
Any client that launches stdio MCP servers works the same way. Have it run
uvx airflow-dev-mcp (or airflow-dev-mcp if you installed it) with the environment
variables below.
Related MCP server: Apache Airflow MCP Server
Variable | Default | Description |
|
| Base URL of the cluster, with no path. |
| — | Username, used together with |
| — | Password. |
|
| HTTP timeout, in seconds. |
|
| Set to |
Set AIRFLOW_USERNAME and AIRFLOW_PASSWORD and you are done; the server works out
whether it is talking to Airflow 3 or Airflow 2 on first use and authenticates the right
way.
Tool | What it does |
| List registered DAGs with their paused, active, and import-error flags. |
| Show parse failures with filename and traceback, so you can see why a new DAG isn't showing up. |
| Pause or unpause a DAG. New local DAGs start paused. |
| Start a manual DAG run, optionally with a |
| Report a run's state and its per-task states (task, state, try number, operator, timing). |
| Return the logs for one task attempt, tailed to the last N lines by default. |
| List recent runs of a DAG, to find a run when you don't have its id. |
| Clear tasks so they re-run. Previews as a dry run by default. |
| Read Airflow Variables. |
| Read Airflow Connections, with passwords omitted. |
Only trigger_dag, set_dag_paused, and clear_task_instances change anything on the
cluster; everything else is read-only. There are no tools that create or modify Variables
or Connections.
MIT, see LICENSE.
If you are interested in contributing see: CONTRIBUTING.md.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
MCP server for AI agents to plan, verify, and deploy Cloudflare-native apps.
MCP server for secureFlows: token-free URL builders and integration-linting tools for AI agents.
MCP server for AI dialogue using various LLM models via AceDataCloud
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/BrianLondon/airflow-dev-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server