Provides comprehensive access to Apache Airflow's REST API, enabling capabilities such as DAG management, task monitoring, log retrieval, and system health diagnostics.
An MCP server that enables AI assistants to interact with Apache Airflow's REST API for DAG management, task monitoring, and system diagnostics. It provides comprehensive tools for triggering workflows, retrieving logs, and inspecting system health across Airflow 2.x and 3.x versions.
31
13
Apache 2.0
Why this server?
Allows interaction with Apache Airflow REST API, providing tools to list DAGs, get DAG runs, trigger runs, check task instances, and monitor scheduler health.
MCP server exposing Apache Airflow REST API operations as tools — list DAGs, inspect runs and task instances, trigger DAG runs, and check failed DAGs and
scheduler health
7
2
MIT
Why this server?
Allows interaction with Apache Airflow within Amazon MWAA, including DAG operations, DAG run monitoring, task instance management, connection and variable access, and retrieving import errors.
MCP server for Amazon MWAA that enables managing Airflow environments, DAGs, runs, task instances, and configurations through natural language, using secure AWS-native API access and read-only by default.
21
Apache 2.0
Why this server?
Provides tools for managing Apache Airflow DAGs, DAG runs, task instances, and logs via the Airflow REST API.
A Model Context Protocol server that exposes Apache Airflow's REST API to AI agents, enabling them to inspect and operate on DAGs, DAG runs, task instances, and logs.
9
MIT
Why this server?
Allows inspection and operation of Apache Airflow over its REST API, providing tools for managing DAGs, DAG runs, task instances, logs, pools, and import errors.
Enables Claude to inspect and operate Apache Airflow over its REST API, providing read tools and safe write operations (gated by read-only mode) for managing DAGs, runs, tasks, and pools.
14
MIT
Why this server?
Reads Apache Airflow DAG source code and provides tools to convert it to Prefect flows, including reading DAG files, looking up translation knowledge, and validating generated code.
An MCP server that converts Apache Airflow DAGs into Prefect flows. It provides tools to read DAGs, lookup translation knowledge, validate code, search Prefect docs, scaffold projects, deploy, and generate migration reports.
7
9
MIT
Why this server?
Provides tools to interact with Apache Airflow instances to inspect DAGs, monitor DAG runs, retrieve task logs, and perform write operations such as triggering DAGs and clearing task instances.
Enables inspection and management of Apache Airflow DAGs, runs, and logs across multiple instances. It provides tools for monitoring workflows and performing gated write operations like triggering DAGs or clearing task instances.
16
2
Apache 2.0
Why this server?
Provides comprehensive tools for managing Amazon MWAA environments and Apache Airflow operations, including DAG management, workflow execution monitoring, task instance tracking, and accessing Airflow connections and variables.
Enables management of Amazon Managed Workflows for Apache Airflow (MWAA) environments and operations including DAG management, workflow execution monitoring, and access to Airflow connections and variables through a unified interface.
21
3
Apache 2.0
Why this server?
Manage Apache Airflow workflows and clusters using natural language commands, including DAG management, task monitoring, pool and variable management, event log analysis, and support for both Airflow 2.x and 3.0+ via dynamic API version selection.
Enables natural language management of Apache Airflow workflows, including DAG monitoring, task control, and configuration, via the Model Context Protocol.
