找到适合你的 MCP Server，快速完成接入

An MCP server that provides access to the Nager.Date API, enabling AI agents to retrieve public holiday information for various countries. It facilitates seamless integration with LLMs through standardized tools and Docker-based deployment.

An open-source MCP server for managing Proxmox environments, including nodes, virtual machines, and containers. It enables users to perform inventory checks, status monitoring, and control operations directly through MCP-compatible tools.

Enables integration between Claude AI and Nasuni environments to monitor appliance health, manage volumes and shares, and generate comprehensive infrastructure reports. Users can interact with the Nasuni Management Center through natural language to track performance metrics and summarize system notifications.

Enables AI agents to deploy applications to AWS with DevOps capabilities by automatically inferring infrastructure needs from code and generating inspectable Infrastructure as Code specifications.

A basic example MCP server demonstrating serverless deployment using Netlify Functions with Express. Shows how to deploy and run MCP servers in a serverless environment with proper routing configuration.

Agentic cloud platform with 45+ MCP tools. Deploy any containerized stack, debug live pods (shell, file editing, DB queries), manage custom domains & TLS, push to built-in container registry, scale pods, and manage GPU workloads. The infrastructure layer where AI agents ship software to production.

A fully local, open-source MCP server that replaces Azure AI Foundry, enabling model management, inference, evaluation, and RAG using Ollama without any cloud dependencies.

全面解析Nhn Server MCPMCP Server的核心功能、安装配置和实用案例。作为顶级Model Context Protocol服务器，Nhn Server MCP能让AI助手访问实时数据、执行操作，为您提供更智能的工作体验和自动化解决方案。

全面解析Nix MCP ServersMCP Server的核心功能、安装配置和实用案例。作为顶级Model Context Protocol服务器，Nix MCP Servers能让AI助手访问实时数据、执行操作，为您提供更智能的工作体验和自动化解决方案。

MCP server that manages npm run dev processes, automatically detecting projects, running them in the background, monitoring logs, and managing ports.

A template repository for bootstrapping Model Context Protocol (MCP) servers with Cloudflare Workers integration, supporting WebSocket and SSE connections with tools, resources, and prompts.

Enables AI assistants to manage Nutanix infrastructure via Prism Central and Prism Element APIs, including VM operations, cluster management, networking, and as-built report generation.

Provides comprehensive monitoring and observability for MCP server ecosystems with real-time health checks, performance metrics, distributed tracing, anomaly detection, and automated performance reports using OpenTelemetry and Prometheus.

Enables deployment of a Model Context Protocol (MCP) server on Cloudflare Workers without authentication. Allows users to create custom tools and connect them to AI clients like Claude Desktop or Cloudflare AI Playground.

Enables LLMs like Claude to interact directly with Oracle Cloud Infrastructure resources, supporting dynamic profile switching between tenancies and comprehensive management of compute instances, database systems, and OCI resources through natural language.

Maple is a unified MCP server for agent observability, safety control, and behavior evolution, acting as a monitoring and auditing layer for high-agency agents. It enables developers to capture session timelines, replay risky branches, detect anomalies using ML, and enforce action guardrails.

A deployable MCP server for Cloudflare Workers or Azure Functions that provides example tools (time, echo, math), prompt templates for code assistance, and configuration resources. Enables AI assistants to interact with edge-deployed services through the Model Context Protocol.

Onigetoc MCP

全面解析MCP Easy InstallerMCP Server的核心功能、安装配置和实用案例。作为顶级Model Context Protocol服务器，MCP Easy Installer能让AI助手访问实时数据、执行操作，为您提供更智能的工作体验和自动化解决方案。

A high-performance MCP server providing up-to-date documentation for Go, npm, Python, Rust, Docker, Kubernetes, Terraform, and more — fetched from official sources, not training data.

Enables users to manage OpenFGA authorization models, stores, and relationship tuples through natural language. It supports permission checks, batch tuple operations, and the deployment of authorization models across different environments.