Airflow MCP Server

Placeholder for the old Airflow package. That is what the airflow mcp server brings to an AI assistant: the same capability, reachable through the Model Context Protocol rather than a separate app or dashboard.

The short version

Apache Airflow (or simply Airflow) is a platform to programmatically author, schedule, and monitor workflows.

Getting it running

apache-airflow on PyPI is all you need. Most clients run it directly, so configuration is a few lines and a restart.

The tools it exposes

The server publishes 14 tools. What each one is for:

• Dynamic — Pipelines are defined in code, enabling dynamic dag generation and parameterization
• Extensible — The Airflow framework includes a wide range of built-in operators and can be extended to fit your needs
• Flexible — Airflow leverages the Jinja templating engine, allowing rich customizations
• Dags — Overview of all Dags in your environment
• Assets — Overview of Assets with dependencies
• Grid — Grid representation of a Dag that spans across time
• Graph — Visualization of a Dag's dependencies and their current status for a specific run
• Home — Summary statistics of your Airflow environment
• Backfill — Backfilling a Dag for a specific date range
• Code — Quick way to view source code of a Dag
• Airflow — SemVer rules apply to core airflow only (excludes any changes to providers)
• Category — Badges

What it needs from you

Apache Airflow is tested with: | | Main version (dev) | Stable version (3.3.0) | Deprecate version (2.11.2) | |------------|------------------------------------|-------------------------------------|------------------------------| | Python | 3.10, 3.11, 3.12, 3.13, 3.14 | 3.10, 3.11, 3.12, 3.13, 3.14 | 3.10, 3.11, 3.12 | | Platform | AMD64/ARM64 | AMD64/ARM64 | AMD64/ARM64(*) | | Kubernetes |

Things to watch

• It runs with your machine's permissions. That is convenient and also the reason to think about what you point it at before you approve a tool call.
• With 14 tools registered it takes up a noticeable share of the context window; turn it off in projects that never touch Airflow.
• Keep per-call confirmation enabled while you learn its behaviour; it is the cheapest safeguard you have.

How it compares

This sits in the developer tooling group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Airflow's toolset — Dynamic, Extensible, Flexible and 11 more — is a fair guide to whether it matches your workflow. It is maintained by Apache Author; worth a glance at recent repository activity before you build anything load-bearing on it.

We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.