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

9 servidores

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Prefect MCP ServerPrefect

Prefect's MCP integration gives agents access to orchestration objects including flows, deployments, work pools, and run diagnostics.

0
Sentry MCP ServerSentry

MCP server for Sentry error tracking integration. Enables AI agents to retrieve and analyze issues, view error stack traces, search events by query, and access project performance data. Helps developers debug production errors by providing contextual error information directly in AI-powered development workflows.

704
Netlify MCP ServerNetlify

Official Netlify MCP server for managing web deployments. Lets AI agents create and configure sites, trigger and monitor deploys, manage environment variables, and read build logs. Useful for shipping frontends and serverless functions, debugging failed builds, and automating deployment workflows from an AI assistant.

470
Jenkins MCP ServerJenkins Community

MCP server for Jenkins CI/CD. Enables AI agents to trigger builds, inspect job and build status, stream console logs, and diagnose failing pipelines, bringing continuous integration workflows into AI-powered development environments.

198
Azure DevOps MCP ServerMicrosoft

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.

0
Buildkite MCP ServerBuildkite

Buildkite's official MCP server for querying and operating CI/CD pipelines, builds, jobs, artifacts, and organization-level delivery workflows.

0
Dynatrace MCP ServerDynatrace

Dynatrace's official MCP server that brings the Dynatrace observability platform into AI workflows. Lets assistants query problems and vulnerabilities, run DQL against logs, metrics, and traces, inspect entities, and pull real-time monitoring data directly into a developer's coding environment for faster troubleshooting and root-cause analysis.

0
Harness MCP ServerHarness

Harness' official MCP server for connecting agents to software delivery workflows, including deployment pipelines, services, environments, and delivery insights.

0
JFrog MCP ServerJFrog

JFrog's MCP server lets agents work with Platform services for artifact repositories, build information, release lifecycle management, and software supply-chain workflows.

0

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.

¿Has creado un servidor MCP?

Envíalo al registro: es de código abierto y está mantenido por la comunidad.

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