The project "kubectl-mcp-server" is a Model Context Protocol (MCP) server designed specifically for Kubernetes environments. It enables AI assistants such as Claude, Cursor, and Windsurf to interact with Kubernetes clusters using natural language commands, thereby simplifying cluster management and operations. The server acts as a bridge between AI clients and Kubernetes, translating natural language queries into Kubernetes API operations and returning responses in an MCP-compliant format. Key features include comprehensive Kubernetes cluster management capabilities such as connecting to clusters, managing pods, services, deployments, nodes, and namespaces. It supports core operations like creating, deleting, describing resources, fetching pod logs, scaling deployments, port forwarding, executing commands inside containers, and managing ConfigMaps and Secrets. The tool also supports Helm v3 operations and advanced Kubernetes features like ingress, network policies, and context switching between clusters. The server processes natural language queries with context awareness, maintaining memory of previous commands to enable intelligent and human-friendly interactions. It can fallback to standard kubectl commands when specialized tools are unavailable and supports mock data for offline or testing scenarios. Monitoring capabilities include cluster health checks, resource utilization tracking, event monitoring, node capacity analysis, and historical performance tracking. Security features encompass RBAC validation, security context auditing, secure API connections, credentials management, network policy assessment, container security scanning, and enforcement of security best practices. Diagnostic tools help with cluster troubleshooting, configuration validation, error analysis, log pattern detection, and resource constraint identification. The server supports multiple transport protocols (stdio, SSE, HTTP) and integrates with multiple AI assistants through an extensible tool framework. The architecture is based on the MCP specification, with components including an MCP server, tools registry, transport layer, core operations translator, and response formatter. It can operate in CLI mode for direct commands or server mode to handle AI assistant requests. Installation is straightforward via PyPI or Docker, with support for multi-architecture images. Overall, kubectl-mcp-server provides a powerful, AI-driven interface for Kubernetes cluster management, leveraging the Model Context Protocol to enable natural language interactions and advanced operational capabilities.
https://github.com/rohitg00/kubectl-mcp-server
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.