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mongodb/mongodb-natural-language-querying

Genera consultas de solo lectura de MongoDB (find) o pipelines de agregación usando lenguaje natural, con contexto del esquema de la colección y documentos de muestra.

¿Qué es mongodb-natural-language-querying?

Esta skill permite a los desarrolladores y usuarios de IA generar consultas de MongoDB y pipelines de agregación en modo solo lectura utilizando lenguaje natural, sin necesidad de conocer la sintaxis exacta de MongoDB. Al proporcionar contexto del esquema de la colección y documentos de muestra, la skill produce consultas precisas y eficientes. Es útil cuando el usuario pide escribir, crear o generar consultas de MongoDB, filtrar, consultar o agregar datos en MongoDB, o traducir solicitudes tipo SQL a sintaxis de MongoDB. No maneja Atlas Search ($search), búsqueda vectorial/semántica ($vectorSearch), coincidencias difusas, índices de autocompletado ni puntuación de relevancia; para esos casos se debe usar la skill search-and-ai. Tampoco analiza ni optimiza consultas existentes (use mongodb-query-optimizer) ni maneja pipelines de agregación que involucren operaciones de escritura. Requiere el servidor MCP de MongoDB.

Compatible con~Claude Code~Codex CLI~Cursor
Part ofAgent Workflows
npx skills add https://github.com/mongodb/agent-skills/tree/main/skills/mongodb-natural-language-querying

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Documentación

¿Qué hace mongodb-natural-language-querying?

Genera consultas de solo lectura de MongoDB (find) o pipelines de agregación usando lenguaje natural, con contexto del esquema de la colección y documentos de muestra. Usa esta skill siempre que el usuario te pida escribir, crear o generar consultas de MongoDB, quiera filtrar/consultar/agregar datos en MongoDB, pregunte "¿cómo consulto...?", necesite ayuda con la sintaxis de consultas, o hable sobre encontrar/filtrar/agrupar documentos de MongoDB. También úsala para traducir solicitudes similares a SQL a sintaxis de MongoDB. NO maneja Atlas Search (operador $search), búsqueda vectorial/semántica (operador $vectorSearch), coincidencias difusas, índices de autocompletado ni puntuación de relevancia; usa search-and-ai para esos casos. NO analiza ni optimiza consultas existentes; usa mongodb-query-optimizer para eso. NO maneja pipelines de agregación que involucren operaciones de escritura. Requiere el servidor MCP de MongoDB.

Individual skills in this repo

This repo contains 5 individual skills — each has its own dedicated page.

mongodb/mongodb-connection

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.

mongodb/mongodb-mcp-setup

Guide users through configuring key MongoDB MCP server options. Use this skill when a user has the MongoDB MCP server installed but hasn't configured the required environment variables, or when they ask about connecting to MongoDB/Atlas and don't have the credentials set up.

mongodb/mongodb-query-optimizer

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/mongodb-schema-design

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".

mongodb/mongodb-search-and-ai

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.

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