Servidores MCP
El registro abierto de servidores Model Context Protocol. Encuentra herramientas, recursos y prompts para tus agentes de IA, filtrados por categoría, transporte o caso de uso.
Servidores
177
Herramientas
471
Categorías
11
Colaboradores
155
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.
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 CircleCI MCP server. Lets AI agents fetch build and pipeline status, retrieve failed build logs, and diagnose flaky or broken jobs so developers can fix CI failures without leaving their AI-powered tools.
Official PagerDuty MCP server for incident management. Lets AI agents list and triage incidents, acknowledge and resolve them, look up on-call schedules, and query services so responders can manage operational incidents from AI-powered tools.
Microsoft's official MCP server for Azure DevOps. Brings Azure DevOps capabilities to AI agents, including managing work items and boards, browsing Git repositories and pull requests, querying builds and pipelines, and accessing wikis and test plans. Lets teams drive their Azure DevOps workflows through natural language from MCP-compatible tools.
Confluent's open-source MCP server that connects AI assistants to Confluent Cloud, Confluent Platform, and standalone Apache Kafka deployments. Provides tools to manage Kafka topics and connectors, work with Schema Registry, and run Flink SQL statements through natural language, helping teams operate streaming data platforms from an MCP client.
Skills frente a servidores MCP
¿cuál es la diferencia?Skillsel «qué hacer»
Una Skill reúne conocimientos prácticos —instrucciones, un prompt de ejemplo y modelos recomendados— para que un agente realice una tarea de forma consistente. Las Skills aportan conocimiento, no nuevas conexiones.
Servidores MCPel «cómo conectarse»
Un servidor MCP proporciona nuevas capacidades a un agente conectándolo con sistemas reales —bases de datos, API o archivos— mediante un transporte. MCP añade conexiones y acciones, no instrucciones de tarea.
Regla práctica: usa una Skill cuando necesites que el modelo realice bien una tarea y un servidor MCP cuando necesites conectarlo con una herramienta o sistema. Se complementan: una Skill puede utilizar las herramientas que proporciona un servidor MCP.
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