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
An MCP server that lets AI assistants delegate work to the Google Gemini CLI, taking advantage of Gemini's very large context window for whole-file and codebase analysis. Exposes tools to ask Gemini questions with file references, brainstorm ideas, and stream long responses in chunks, so a client model can offload large-context reasoning to Gemini without leaving the current session.
Google's official collection of Model Context Protocol servers that expose Google's security products to MCP clients. Provides access to Google SecOps (Chronicle) for SIEM search and case management, Google Threat Intelligence for indicator and malware lookups, and Security Command Center for cloud posture and findings. Lets security agents investigate alerts, enrich indicators of compromise, and triage vulnerabilities directly from natural language.
AgentMail MCP gives AI agents secure email operations through hosted MCP access and local stdio bridges for agent-native inbox workflows.
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.
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.
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.
Dagster's MCP integration enables agents to inspect and operate data assets, jobs, schedules, and runs in modern data platforms.
The official MCP reference and test server demonstrates tools, resources, prompts, sampling, and protocol capabilities for MCP client development.
Firebase's MCP integration exposes Firebase project context and development workflows to coding agents for building, testing, and operating Firebase applications.
Fivetran's MCP server helps agents inspect data connectors and sync health, diagnose broken connections, and answer operational questions about managed ELT pipelines.
Google's official MCP server for the Google Ads API, exposing resources and tools that let agents analyze advertising accounts and support campaign operations.
GrowthBook's official MCP server for working with feature flags, experiments, and metrics so agents can safely support product experimentation workflows.
Harness' official MCP server for connecting agents to software delivery workflows, including deployment pipelines, services, environments, and delivery insights.
InfluxData's MCP server connects agents to InfluxDB 3 for SQL queries, schema exploration, and time-series database operations.
JetBrains' official MCP server for working with IntelliJ-based IDEs and Android Studio, enabling agents to inspect projects and use IDE-aware development capabilities.
JFrog's MCP server lets agents work with Platform services for artifact repositories, build information, release lifecycle management, and software supply-chain workflows.
Microsoft's MarkItDown MCP integration converts documents from HTTP, file, and data URIs into Markdown for agent-ready analysis and retrieval.
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?
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