"Apache Airflow" matching MCP servers:

Enables users to interact with Apache Airflow through MCP to monitor DAGs, inspect runs and logs, and diagnose task failures using natural language.

Enables natural language management of Apache Airflow workflows, including DAG monitoring, task control, and configuration, via the Model Context Protocol.

Provides a standardized way for MCP clients to interact with Apache Airflow's REST API, supporting operations like DAG management and monitoring Airflow system health.

An MCP server for controlling Apache Airflow 3 via its REST API, enabling operations like DAG management, task monitoring, and more through natural language.

Enables natural language interaction with Apache Airflow for querying DAGs, monitoring execution, and troubleshooting failures.

Enables interaction with Apache Airflow through the Model Context Protocol, allowing users to manage DAGs, task instances, variables, connections, pools, XComs, and datasets.

Enables DAG management, monitoring, debugging, and connection testing for Apache Airflow through the MCP protocol.

An MCP server that enables AI coding assistants to interact with a local Airflow cluster via its REST API for triggering DAG runs, monitoring status, reading logs, and diagnosing errors.

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.

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

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.

Airflow MCP server — read DAGs, runs, task instances, log tails; trigger and clear (write-gated).

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.

https://github.com/abhishekbhakat/airflow-mcp-server

Enables LLM agents to interact with Apache Zeppelin notebooks via the Zeppelin REST API, supporting operations like listing, searching, editing, and running notebooks.

An MCP Server for controlling Airflow via Airflow APIs.

A Model Context Protocol server for controlling Airflow 3 via Airflow APIs.

MCP server that diagnoses Apache Spark job failures and optimizes performance using stack-trace analysis and LLM providers, supporting EMR and local sources.

A tool that converts natural-language requests into full, deployable, execution-verified Apache Flink SQL jobs by orchestrating an LLM to draft specs, generate Flink SQL, and verify correctness against real Flink clusters and streaming semantics—including windowed aggregates, joins, Kafka grounding, and Deployable pipelines via the generate_flink_job, ground_kafka_topic, and deploy_to_kafka tools.

MCP server for chatting with physical-world data from robotics, drones, automotive, and IoT sources using natural language. It generates auditable SQL queries over Apache Arrow/DuckDB to let you analyze, summarize, and build data pipelines.