Discover verified Model Context Protocol servers to extend your AI assistants. Install configs for Claude Code, Cursor, VS Code, and more.
Browse 260 MCP servers across databases, communication platforms, cloud providers, DevOps pipelines, analytics services, and version control systems. Each server entry includes verified compatibility badges, GitHub star counts, transport type, install commands, and security notes. The Model Context Protocol (MCP) is an open standard that lets AI assistants like Claude, Cursor, and GitHub Copilot interact with external tools and data sources through a unified interface — find the right server to connect your AI workflow to the services you already use.
Query Stripe data including customers, payments, subscriptions, and invoices from AI assistants.
Deploy and manage Cloudflare Workers, KV namespaces, R2 buckets, and D1 databases via MCP.
Interact with AWS services including S3, Lambda, DynamoDB, and CloudFormation via MCP.
Deploy and manage Cloudflare Workers AI workloads from your AI assistant. Run AI models on Cloudflare's global edge network, manage Workers AI bindings, configure Vectorize indexes, and deploy AI-powered Workers. The server provides access to Cloudflare's AI infrastructure — running inference on 50+ models at the edge with low latency. Different from the general Cloudflare MCP, this focuses specifically on AI workloads: model selection, Vectorize for RAG, AI Gateway for observability, and Workers AI bindings.
Manage Cloudflare Workers, KV, R2, D1, and DNS from AI assistants via MCP.
Manage Vercel deployments, projects, domains, and environment variables via AI assistants.
Manage AWS Lambda functions from your AI coding assistant. Deploy function code, update configurations, invoke functions, view CloudWatch logs, manage layers, and configure triggers. The server provides comprehensive Lambda lifecycle management — from development through production monitoring. Your AI can help debug invocation errors by reading logs, optimize memory/timeout settings based on metrics, and manage deployment aliases for safe rollouts.
Manage Coolify self-hosted platform deployments from your AI coding assistant. Coolify is an open-source, self-hostable alternative to Heroku/Netlify/Vercel. This server lets you deploy applications, manage databases, configure domains, and monitor services all running on your own infrastructure. Tools include application deployment from Git, database provisioning (PostgreSQL, MySQL, Redis, MongoDB), SSL certificate management, and server resource monitoring.
Manage cloud infrastructure with Pulumi through AI assistants, supporting stacks, previews, and deployments.
Deploy and manage applications on Deno Deploy from your AI coding assistant. Push code, manage projects, configure KV databases, set environment variables, and monitor deployments. The server leverages Deno Deploy's instant global deployment model — code deploys in seconds to 35+ edge locations. Your AI can help manage Deno KV state, configure cron jobs, manage fresh/hono routes, and monitor edge function performance across regions.
Manage your Tailscale mesh VPN network from your AI coding assistant. View connected devices, manage ACLs, configure exit nodes, check device status, and manage DNS settings. The server provides visibility into your Tailscale network — see which machines are online, troubleshoot connectivity issues, manage network access policies, and configure routes. Useful for developers managing distributed infrastructure where Tailscale provides secure networking between services.
Run GPU workloads on Modal's serverless platform from your AI coding assistant. Define and deploy functions, manage secrets, monitor running jobs, and access GPU resources (A100, H100) without infrastructure management. The server enables AI-assisted GPU computing workflows — your assistant can deploy model inference endpoints, run batch processing jobs, manage container environments, and monitor compute costs. Particularly useful for ML engineers who want to quickly iterate on GPU-intensive tasks.
Deploy sites, manage builds, and configure Netlify services through AI assistants via the Model Context Protocol.
Manage Amazon ECS (Elastic Container Service) clusters, services, and tasks from your AI assistant. View service status, update task definitions, trigger deployments, read container logs, and manage scaling policies. The server provides operational visibility into containerized workloads — your AI can help diagnose unhealthy tasks, optimize resource allocation, manage rolling deployments, and troubleshoot service connectivity issues without navigating the AWS Console.
Deploy and manage Google Cloud Run services from your AI coding assistant. Deploy container images, manage revisions, configure traffic splitting, set environment variables, and view logs. The server handles the full Cloud Run lifecycle — from initial deployment through production traffic management. Your AI can help with canary deployments (split traffic between revisions), debug cold start issues, optimize concurrency settings, and manage custom domains.
Deploy and manage Azure Functions from your AI coding assistant. Create function apps, deploy code, configure triggers and bindings, view execution logs, and manage application settings. The server supports all Azure Functions triggers (HTTP, Timer, Blob, Queue, Event Hub) and provides tools for monitoring executions, managing slots for deployment, and configuring Durable Functions workflows.
Manage Docker environments through Portainer's API from your AI coding assistant. Control containers, images, networks, and volumes across multiple Docker hosts and Kubernetes clusters. The server provides tools for container lifecycle management, image pulling and building, stack deployment, and resource monitoring. Portainer's multi-environment management makes this server particularly valuable for teams managing containers across development, staging, and production environments.
Manage Google Cloud Storage buckets, objects, and access controls through the Model Context Protocol.
Manage Appwrite backend services including databases, storage, functions, and authentication via MCP.
Plan, apply, and manage Terraform infrastructure configurations through AI assistants.
Manage DigitalOcean droplets, databases, Kubernetes clusters, and app platform deployments via MCP.
Manage Azure Blob Storage containers, blobs, and access policies through AI assistants via MCP.
Manage Azure resources and services including VMs, storage, and functions through MCP.
Manage Shopify products, orders, and store data through AI assistants via MCP.
Manage GCP resources, Cloud Run services, and Pub/Sub topics through the Model Context Protocol.
Deploy and manage Supabase Edge Functions, invoke them remotely, and monitor execution logs.
Manage Kubernetes clusters, pods, deployments, and services via AI assistants through MCP.
Deploy and manage GitHub Pages sites, configure custom domains, and monitor build status via MCP.
Manage Hetzner Cloud servers, networks, load balancers, and firewalls through the Model Context Protocol.
Manage Supabase authentication users, providers, and security policies through MCP.
Interact with Firebase services including Firestore, Auth, and Cloud Functions via MCP.
Access OpenAI APIs for text generation, embeddings, and image creation through AI assistants via MCP.
Provision and manage Linode instances, volumes, and networking resources through AI assistants.
Access Anthropic Claude APIs for text generation and analysis through the Model Context Protocol.
Upload, download, and list objects in AWS S3 buckets through AI assistants via MCP.
Manage OVHcloud infrastructure including dedicated servers, public cloud instances, and domain services.
Deploy projects, manage services, and configure environments on Railway through the Model Context Protocol.
Deploy and manage Render services, databases, and cron jobs through AI assistants via MCP.
Deploy and manage Fly.io applications, machines, and volumes through the Model Context Protocol.
