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 Airtable that lets AI agents read and write records, list bases and tables, inspect field schemas, and run filtered queries. Ideal for turning Airtable into a lightweight backend or knowledge base that agents can manage conversationally.
MCP server for Google BigQuery that lets AI agents explore datasets, inspect table schemas, and run SQL analytics queries with dry-run cost estimation. Useful for natural-language data analysis, ad-hoc reporting, and pipeline debugging against large-scale BigQuery warehouses.
Official GitLab MCP server connecting AI tools to GitLab's DevOps platform. Enables agents to manage projects, issues, merge requests, branches, files, CI/CD pipelines, and the GitLab Duo workflow. Supports both GitLab.com SaaS and self-managed instances with fine-grained access tokens.
Official HubSpot MCP server connecting AI tools to the HubSpot CRM. Enables agents to read and manage contacts, companies, deals, and tickets, search the CRM, and create engagements such as notes and tasks. Honors HubSpot scopes and rate limits via OAuth.
MCP server for Salesforce that lets AI agents query and modify CRM data using SOQL, manage standard and custom objects (Accounts, Contacts, Opportunities, Cases), describe object metadata, and execute Apex anonymous blocks. Supports both production and sandbox orgs via OAuth or username-password flows.
Official Snowflake MCP server enabling AI agents to query and analyze data in the Snowflake AI Data Cloud. Supports running SQL against warehouses, exploring databases and schemas, describing tables, and invoking Cortex AI services for search and analytics, with role-based access control honored end to end.
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.
MCP server that fetches transcripts, captions, and metadata from YouTube videos so AI agents can summarize, search, and analyze video content without watching it. Supports multiple languages, timestamped segments, and channel/playlist lookups for research and content 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.
A Model Context Protocol server that enables AI agents to interact with Discord through a bot. Agents can send and read messages in channels, list servers and channels, and manage basic server interactions while keeping the user in control. Useful for community automation, notifications, and conversational workflows on Discord.
E2B's MCP server gives AI agents the ability to run arbitrary code in secure, isolated cloud sandboxes. Each sandbox is a fast-booting micro-VM where models can execute Python and shell commands, install packages, read and write files, and capture stdout/stderr — ideal for code interpretation, data analysis, and agentic workflows that need real execution.
A Model Context Protocol server for Google Drive that lets AI assistants list, search, and read files stored in Drive, with automatic export of Google Docs, Sheets, Slides, and Drawings to readable formats. Authenticates via OAuth 2.0 and exposes Drive files as MCP resources so agents can ground their answers in your documents without copying data into chat first.
Pinecone's official MCP server connects AI assistants to Pinecone vector databases for retrieval-augmented generation (RAG) workflows. Lets agents create and configure indexes, upsert and embed documents, and run semantic searches over vector data — all from natural language, without leaving the editor or chat.
The official Shopify Dev MCP server, built by Shopify, gives AI coding tools direct access to Shopify's developer platform. Agents can search Shopify documentation, explore and introspect the Admin and Storefront GraphQL API schemas, validate GraphQL operations, and scaffold Functions — keeping answers grounded in up-to-date, accurate Shopify APIs.
Official Atlassian Rovo MCP Server — a cloud-based bridge between Atlassian Cloud and any MCP-compatible AI tool. Enables AI agents to search, summarize, create, and update Jira issues, Confluence pages, and Compass components in real-time. Uses OAuth 2.1 or API token authentication, respects existing user permissions, and supports remote HTTP-streaming as well as local stdio via the mcp-remote proxy. Works with Claude, GitHub Copilot, Gemini CLI, VS Code, Cursor, and ChatGPT.
Official Elastic MCP server that connects AI agents to Elasticsearch data using the Model Context Protocol. Enables natural language interactions with Elasticsearch indices — querying, analyzing, and retrieving data without custom APIs. Supports both stdio and streamable-HTTP transports, and works with Elasticsearch 8.x/9.x clusters including Elasticsearch Serverless. Distributed as a Docker container image from the Elastic registry.
Official Hugging Face MCP Server that connects AI assistants directly to the Hugging Face Hub ecosystem. Provides tools for searching and retrieving models, datasets, and research papers, running inference on thousands of Gradio-powered AI applications (Spaces), and accessing the full Hub API. Supports remote HTTP-streaming via https://huggingface.co/mcp with OAuth or Bearer token auth, as well as local stdio deployment. Works with Claude, Gemini CLI, VS Code, Cursor, and any MCP-compatible client.
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.
A VSCode extension that turns your running VS Code instance into an MCP server, giving external AI agents (Claude Desktop, Claude Code, and others) direct access to VS Code's editing, navigation, and debugging capabilities. Supports reviewing code changes through diffs, real-time diagnostic streaming (type errors, lint warnings), terminal command execution, URL preview in the built-in browser, debug session management, and multi-window instance switching. Also relays built-in MCP servers introduced in VS Code 1.99, including GitHub Copilot tools.
Official Anthropic MCP server providing direct access to Claude models through the Model Context Protocol. Enables AI agents to invoke Claude for sub-tasks like summarization, analysis, and code generation within agentic workflows. Supports streaming responses, system prompts, and multi-turn conversations.
Provides AI agents with access to PostHog product analytics through the Model Context Protocol. Enables querying events, analyzing funnels, inspecting feature flags, and reviewing session recordings metadata. Supports HogQL queries for advanced analytics and cohort analysis for user segmentation.
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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