mcp-server-apache-airflow is a Python MCP server that wraps the Apache Airflow REST API so MCP clients can drive an Airflow deployment through a standard protocol. It builds on the official Apache Airflow client library rather than hand-rolled HTTP calls, which keeps request and response handling aligned with the upstream API and easier to maintain. Coverage is broad and documented as a status table mapping each supported feature to its /api/v1 path. DAG management includes listing DAGs, reading DAG details, pausing and unpausing, updating and deleting a DAG, patching several DAGs at once, fetching DAG source by file token and asking the scheduler to reparse a DAG file. DAG run tools list and create runs, read, update and delete a specific run, fetch runs in batch, clear a run, attach a note to it and list upstream dataset events. Task tools list the tasks of a DAG and read a task definition, read a single task instance or list the instances of a run, update an instance, fetch the log for a given try number, list the tries of an instance, clear task instances and set their state in bulk. Administrative objects are covered too: variables can be listed, created, read, updated and deleted; connections support the same lifecycle plus a test operation; and pools can be listed, created and read. Taken together this means an assistant can inspect why a pipeline failed, read the relevant task logs, clear and rerun the affected tasks, and adjust the variables or connections involved without leaving the client. The package is published on PyPI and can also be installed through Smithery. It targets data and platform engineers who already run Airflow and want conversational access to it from an MCP-capable client.
https://github.com/yangkyeongmo/mcp-server-apache-airflow
Apache APISIX is a high-performance cloud-native API Gateway and AI Gateway that supports Model Context Protocol (MCP) integration through its mcp-bridge plugin, offering dynamic traffic management, security, and observability features.
AWS MCP Servers is a suite of specialized Model Context Protocol servers that integrate AWS best practices and resources into AI-powered development workflows to enhance cloud-native development and infrastructure management.
Webiny is an open-source, self-hosted headless CMS and framework, built on AWS serverless with AI-assisted development capabilities via an embedded MCP server, designed for large organizations.
MCP Registry is a community-driven centralized service offering a RESTful API for discovering, managing, and publishing Model Context Protocol (MCP) servers with MongoDB and Docker support.
Olares is an open-source personal cloud operating system that enables users to run local AI models and applications with personalized, context-aware AI interactions using a Model Context Protocol-like mechanism, providing a private and secure AI home server environment.
Cloudflare MCP Server is a suite of MCP servers that enable large language model clients to interact with and manage Cloudflare's cloud services through natural language commands, facilitating automation and insights across various domains.
mcp-grafana is an MCP server that provides comprehensive programmatic access to Grafana's dashboards, datasources, alerts, incidents, and on-call management features through MCP clients.
MCP Containers offers containerized versions of hundreds of MCP servers, enabling easy, secure, and up-to-date deployment of Model Context Protocol servers for AI integration and experimentation.