Cloudflare MCP Server

MCP servers for AWS cloud services and workflows

AWS MCP Servers is a collection of open-source MCP server implementations from AWS Labs that connect AI coding agents to AWS services. It includes servers for AWS documentation, knowledge bases, and managed cloud workflows, enabling agents to provision resources, query docs, and manage infrastructure through the Model Context Protocol.

Official MCP server for GitHub repo operations

GitHub MCP Server is the official Model Context Protocol server from GitHub that connects AI assistants to repositories, issues, pull requests, workflows, and code search. It exposes 100+ operations with toolset filtering, permission scoping, and audit logging, available in both remote-hosted and self-hosted Docker deployment modes.

Run your app close to users

Global application hosting platform that runs full-stack apps, databases, and services close to users in 30+ regions using lightweight Firecracker micro-VMs. Deploy Docker containers or use built-in builders for Rails, Laravel, Django, Node.js, and Go. Features persistent volumes, private networking, auto-scaling, PostgreSQL/Redis managed databases, and a powerful CLI. Ideal for latency-sensitive apps needing multi-region presence. Generous free allowance with pay-as-you-go pricing.

Infrastructure, instantly

Modern cloud platform for deploying full-stack apps, databases, and workers with instant provisioning and usage-based pricing. Deploy from GitHub or CLI with zero config for Node.js, Python, Go, Rust, and Docker. Built-in PostgreSQL, MySQL, Redis, and MongoDB with auto backups. Features private networking, environment management, cron jobs, TCP proxying, and real-time logs. Popular with indie hackers and startups for fast MVPs with a generous free trial including $5 monthly credits.

Open standard for portable skills across AI agents

Agent Skills is the open SKILL.md folder specification for packaging reusable instructions, scripts, references, and assets that compatible AI agents load through progressive disclosure. Originally developed by Anthropic and released as an open standard, it defines the portable format itself—not an example library, marketplace, or hosted agent product.

Heterogeneous CPU-GPU inference and SFT for large MoE models

Open-source framework for running and fine-tuning large Mixture-of-Experts models with heterogeneous CPU-GPU execution, optimized kernels, limited VRAM and SGLang or LLaMA-Factory integrations.

Official Kubernetes and Helm reference stack built on the vLLM inference engine

Official vLLM reference implementation for scaling the existing inference engine on Kubernetes with Helm, request routing, KV-cache offload, autoscaling and Prometheus/Grafana observability.

Distributed inference orchestration above vLLM, SGLang and TensorRT-LLM

Open-source, datacenter-scale orchestration layer that coordinates vLLM, SGLang and TensorRT-LLM across nodes with disaggregated serving, KV-aware routing, multi-tier cache management and automatic scaling.

Open-source GPU control plane for scalable AI model serving

Open-source GPU cluster manager that configures vLLM, SGLang, TensorRT-LLM or custom engines, serves models through compatible APIs, and provisions SSH-accessible GPU instances across on-premises, Kubernetes and cloud environments.

Test and debug MCP servers before they ship

Open-source platform for inspecting, debugging and regression-testing MCP servers, MCP Apps and ChatGPT apps, with OAuth and protocol conformance for local and CI workflows.