Guía a los usuarios en la configuración de las opciones clave del servidor MCP de MongoDB. Usa esta habilidad cuando el usuario tenga el servidor MCP de MongoDB instalado pero no haya configurado las variables de entorno necesarias, o cuando pregunte sobre cómo conectarse a MongoDB/Atlas y no tenga las credenciales configuradas.
La habilidad mongodb/mongodb-mcp-setup está diseñada para ayudar a los desarrolladores a configurar correctamente el servidor MCP (Model Context Protocol) de MongoDB. Su propósito principal es guiar a los usuarios a través de la configuración de variables de entorno esenciales, como la cadena de conexión a MongoDB (MONGODB_URI) y otras opciones del servidor MCP. Esta habilidad es particularmente útil en entornos de desarrollo como Claude Code, Cursor y Codex, donde la integración con bases de datos MongoDB o Atlas es necesaria. Al ejecutarse, proporciona instrucciones paso a paso y comandos de terminal para asegurar que el servidor MCP quede correctamente instalado y funcional, reduciendo la fricción en la configuración inicial. También ayuda a diagnosticar y resolver problemas comunes, como la falta de credenciales o conexiones fallidas, mejorando la experiencia del desarrollador al trabajar con MongoDB en aplicaciones basadas en agentes de IA.
npx skills add https://github.com/mongodb/agent-skills/tree/main/skills/mongodb-mcp-setupInstalled? Explore more Productividad y colaboración skills: steipete/gemini, steipete/gh-issues, steipete/skill-creator · View all 6 →
Guía a los usuarios en la configuración de las opciones clave del servidor MCP de MongoDB. Usa esta habilidad cuando el usuario tenga el servidor MCP de MongoDB instalado pero no haya configurado las variables de entorno necesarias, o cuando pregunte sobre cómo conectarse a MongoDB/Atlas y no tenga las credenciales configuradas.
This repo contains 5 individual skills — each has its own dedicated page.
Optimize MongoDB client connection configuration (pools, timeouts, patterns) for any supported driver language. Use this skill when working/updating/reviewing on functions that instantiate or configure a MongoDB client (eg, when calling `connect()`), configuring connection pools, troubleshooting connection errors (ECONNREFUSED, timeouts, pool exhaustion), optimizing performance issues related to connections. This includes scenarios like building serverless functions with MongoDB, creating API endpoints that use MongoDB, optimizing high-traffic MongoDB applications, creating long-running tasks and concurrency, or debugging connection-related failures.
Generate read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks "how do I query...", needs help with query syntax, or discusses finding/filtering/grouping MongoDB documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or relevance scoring - use search-and-ai for those. Does NOT analyze or optimize existing queries - use mongodb-query-optimizer for that. Does NOT handle aggregation pipelines that involve write operations. Requires MongoDB MCP server.
Help with MongoDB query optimization and indexing. Use only when the user asks for optimization or performance: "How do I optimize this query?", "How do I index this?", "Why is this query slow?", "Can you fix my slow queries?", "What are the slow queries on my cluster?", etc. Do not invoke for general MongoDB query writing unless user asks for performance or index help. Prefer indexing as optimization strategy. Use MongoDB MCP when available.
MongoDB schema design patterns and anti-patterns. Use when designing data models, reviewing schemas, migrating from SQL, or troubleshooting performance issues caused by schema problems. Triggers on "design schema", "embed vs reference", "MongoDB data model", "schema review", "unbounded arrays", "one-to-many", "tree structure", "16MB limit", "schema validation", "JSON Schema", "time series", "schema migration", "polymorphic", "TTL", "data lifecycle", "archive", "index explosion", "unnecessary indexes", "approximation pattern", "document versioning".
Guides MongoDB users through implementing and optimizing Atlas Search (full-text), Vector Search (semantic), and Hybrid Search solutions. Use this skill when users need to build search functionality for text-based queries (autocomplete, fuzzy matching, faceted search), semantic similarity (embeddings, RAG applications), or combined approaches. Also use when users need text containment, substring matching ('contains', 'includes', 'appears in'), case-insensitive or multi-field text search, or filtering across many fields with variable combinations. Provides workflows for selecting the right search type, creating indexes, constructing queries, and optimizing performance using the MongoDB MCP server.
Gemini CLI one-shot prompts, summaries, generation, skills, hooks, MCP, or Gemma routing.
Fetch GitHub issues, select candidates, spawn background fix agents, open PRs, and optionally process PR review comments.
Create, edit, audit, tidy, validate, or restructure AgentSkills and SKILL.md files.
Search, install, update, sync, or publish agent skills with the ClawHub CLI and registry.
Delegate coding work to Codex, Claude Code, or OpenCode as background workers; not simple edits or read-only code lookup.
List, configure, authenticate, call, and inspect MCP servers/tools with mcporter over HTTP or stdio.