MCP Servers
The open registry for Model Context Protocol servers. Find the right tools, resources, and prompts for your AI agents — filtered by category, transport, or use case.
Servers
92
Tools
343
Categories
11
Contributors
79
Official Vercel MCP server that gives AI tools secure access to Vercel projects via OAuth. Enables searching Vercel documentation, managing projects and deployments, analyzing deployment logs, and interacting with Vercel infrastructure. Supports Streamable HTTP transport with OAuth authentication and integrates with Claude Code, Cursor, VS Code, ChatGPT, Codex CLI, and other AI assistants.
Provides access to AWS documentation and service information through the Model Context Protocol. Enables AI agents to search AWS documentation, retrieve service descriptions, look up API references, and find best practices for AWS services. Part of the official AWS Labs MCP servers collection with multiple AWS-focused servers available.
Enables AI agents to interact with Amazon Web Services through the Model Context Protocol. Provides access to core AWS services including S3, Lambda, DynamoDB, CloudWatch, and IAM. Supports resource discovery, log analysis, and infrastructure management with proper credential handling and region awareness.
Provides AI agents with the ability to manage Terraform infrastructure through the Model Context Protocol. Supports plan generation, state inspection, resource drift detection, and module discovery. Enables safe infrastructure changes with plan review before apply.
A powerful, native Go implementation of a Kubernetes MCP server with support for Kubernetes and OpenShift. Unlike kubectl wrappers, it interacts directly with the Kubernetes API server — no external CLI tools required. Distributed as a single lightweight binary for Linux, macOS, and Windows. Supports multi-cluster configurations, Helm chart management, Tekton pipelines, pod exec, log streaming, and optional OpenTelemetry distributed tracing.
Official Docker MCP server for container management. Enables AI agents to list, start, stop, and inspect Docker containers, manage images, view logs, and execute commands inside running containers. Supports Docker Compose operations for multi-container applications and provides container health monitoring capabilities.
The official Azure MCP Server brings Microsoft Azure to AI agents. It lets models query and manage Azure resources through natural language — Storage blobs and tables, Cosmos DB, Azure SQL, Key Vault, Monitor/Log Analytics (KQL), App Configuration, and more — and run Azure CLI commands, enabling cloud automation and infrastructure workflows directly from your tools.
Official Snyk MCP server that brings developer security scanning into AI agent workflows. Lets agents scan code, open-source dependencies, containers, and infrastructure-as-code for vulnerabilities, retrieve fix advice, and check license issues directly from the editor or CI, using the Snyk CLI under the hood.
Official MCP server for the Stripe API. Enables AI agents to interact with Stripe's payment infrastructure including creating and managing customers, payment intents, subscriptions, invoices, and products. Supports reading transaction data, handling refunds, and querying balance information. Useful for building payment integrations, debugging billing issues, and automating financial operations.
Skills vs MCP servers
what's the difference?Skillsthe “what to do”
A skillA reusable, structured prompt/workflow with recommended models, an example prompt, and compatible tools. packages know-how — instructions, an example promptA ready-to-use prompt template that demonstrates how to invoke the skill., and recommended models — so an agent performs a task consistently. Skills add knowledge, not new connections.
MCP serversthe “how to connect”
An MCP serverModel Context Protocol server — a standard way to expose tools, resources, and prompts to AI agents and IDEs. gives an agent new capabilities by connecting it to real systems (databases, APIs, files) over a transportHow the client talks to the server: stdio (local process), SSE, or HTTP streaming.. MCP adds connections and actions, not task instructions.
Rule of thumb: reach for a skill when you need the model to do a task well, and an MCP server when you need it to reach a tool or system. They compose — a skill can rely on tools an MCP server provides.
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