docker-mcp is a Model Context Protocol (MCP) server designed to facilitate Docker container and Docker Compose stack management through integration with Claude AI. It provides a set of tools that enable users to create and instantiate Docker containers, deploy Docker Compose stacks, retrieve container logs, and monitor container statuses seamlessly. The project aims to simplify Docker operations by leveraging the MCP framework, allowing for efficient and automated container management workflows. The server supports key functionalities such as creating standalone Docker containers with customizable options like image selection, container naming, port mapping, and environment variable settings. It also allows for the deployment of Docker Compose stacks by specifying project names and Compose YAML configurations. Users can retrieve logs from specific containers to analyze runtime behavior and issues, as well as list all running Docker containers to monitor their status and health. docker-mcp is built with Python 3.12+ and requires Docker Desktop or Docker Engine to operate. It integrates with Claude Desktop, a client application, through configuration files that specify the MCP server commands and arguments. The project supports installation via Smithery, a package manager for MCP servers, enabling easy setup and updates. While docker-mcp offers robust container management features, it currently has some limitations, including the lack of built-in support for environment variables in containers, volume management, network management, container health checks, restart policies, and resource limits. Despite these limitations, the project provides a solid foundation for managing Docker environments through AI-driven protocols. The repository includes detailed documentation on installation, configuration, development setup, and debugging using the MCP Inspector tool. It encourages community contributions and is licensed under the MIT License. The project is maintained by core contributors Alex Andru and Ali Sadykov. Keywords associated with this project include Model Context Protocol, MCP server, Docker, Docker Compose, container management, Claude AI, container creation, stack deployment, log retrieval, container monitoring, Python, and Smithery.
https://github.com/QuantGeekDev/docker-mcp
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.