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
160
Tools
465
Categories
11
Contributors
142
Infisical's official MCP server for its open-source secret management platform. Lets AI agents list projects and environments and create, read, update, and delete secrets and folders through controlled tools, so assistants can help manage application configuration without exposing raw credentials in code. Authenticates using a machine identity.
GitHub's official MCP Server that connects AI tools directly to GitHub's platform. Enables AI agents to manage repositories, issues, pull requests, branches, files, actions workflows, and code security. Supports both remote (OAuth) and local (Docker/binary) modes with fine-grained toolset configuration.
Connects AI agents to Datadog for monitoring, observability, and incident management. Enables querying metrics, viewing traces, searching logs, and managing monitors programmatically. Supports dashboard creation, alert configuration, and SLO tracking through the Model Context Protocol.
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.
Integrates Twilio communication APIs with AI agents through the Model Context Protocol. Enables sending SMS messages, making voice calls, managing phone numbers, and querying message history. Supports programmable messaging, conversation management, and webhook configuration for real-time communication workflows.
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.
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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