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
MCP server for Intercom. Lets AI agents search and read conversations and contacts, retrieve message history, and query help-center articles via the Intercom API. Useful for support analytics, conversation summarization, and building assistants that reason over customer communications.
Provides access to the Slack API through the Model Context Protocol. Enables AI agents to read and send messages, manage channels, search conversation history, and interact with Slack workspaces. Supports listing channels, reading threads, posting messages, and adding reactions programmatically. Originally maintained by Anthropic, now maintained by Zencoder.
AgentMail MCP gives AI agents secure email operations through hosted MCP access and local stdio bridges for agent-native inbox workflows.
Algolia's MCP server for interacting with the Algolia search platform through AI tools. Lets agents run searches against indices, inspect and manage index settings and records, and review analytics and monitoring data using natural language. Useful for building, tuning, and debugging search experiences powered by Algolia.
Apache Doris' MCP server provides agents with governed access to Doris analytics databases for schema discovery, query assistance, and operational investigation.
Apache IoTDB's MCP server connects agents to time-series data and metadata for operational analysis, industrial telemetry exploration, and query assistance.
Apache SkyWalking's MCP server gives agents access to observability data for tracing, service topology, metrics, logs, and production incident investigation.
Apache Solr's MCP server connects agents to search collections and query workflows for schema discovery, relevance investigation, and search application development.
Apollo's official MCP server that exposes GraphQL operations as MCP tools, letting AI agents interact with any GraphQL API through the Model Context Protocol. Turns curated GraphQL operations into callable tools, supports schema introspection, and can run locally alongside a graph via the Rover CLI or in production with the Apollo Runtime Container.
DataStax's official MCP server for Astra DB, a serverless database built on Apache Cassandra with native vector search. Lets AI agents create and manage collections, insert and update records, and run similarity and metadata queries, making it a convenient backend for retrieval-augmented generation and agent memory workloads.
Atlassian's official MCP server provides authenticated access to Jira, Confluence, Jira Service Management, Bitbucket, and Compass workflows.
Auth0's official MCP server that connects AI agents to the Auth0 Management API. Lets developers create and configure applications, deploy Actions, manage APIs and connections, and query tenant logs using natural language. Runs locally with interactive tenant authentication and stores access tokens in the system keychain so the model never sees them directly, supporting secure Auth0 administration.
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.
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.
BrowserStack's official MCP server for AI-assisted testing. Lets agents manage test cases, run manual and automated tests across real browsers and devices, access debugging artifacts such as logs and session details, and triage failures using plain English. Useful for cross-platform QA and browser-compatibility workflows from MCP-enabled clients.
Buildkite's official MCP server for querying and operating CI/CD pipelines, builds, jobs, artifacts, and organization-level delivery workflows.
Chrome's official MCP server for inspecting and controlling a live browser through Chrome DevTools. It helps agents diagnose UI, network, performance, and runtime issues.
Cloudinary's official MCP servers for managing media through conversational AI. Cover the full media workflow: uploading and transforming images and videos, organizing assets with structured metadata, configuring processing pipelines, and running AI-powered content analysis. Available as remote OAuth endpoints or local npx processes across several focused servers (asset management, environment config, structured metadata, and analysis).
Google Cloud's MCP server for deploying and managing applications on Cloud Run, giving coding agents a guided path from source code to a serverless production service.
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.
Contentful's official MCP server for the Content Management API. Gives AI agents tools to create, edit, organize, and publish content, manage content models and assets, and work across spaces and environments. Useful for automating editorial workflows and content operations in a Contentful-backed headless CMS.
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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