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mongodb/mongodb-connection

Otimize a configuração de conexão do cliente MongoDB (pools, timeouts, padrões) para qualquer linguagem de driver suportada.

O que é mongodb-connection?

Esta skill foca na otimização e configuração de conexões com o MongoDB, abrangendo drivers de várias linguagens. Ela é essencial para desenvolvedores que trabalham com a criação ou manutenção de funções que instanciam ou configuram um cliente MongoDB, como ao chamar `connect()`, ou que lidam com pools de conexão e timeouts. A skill é particularmente útil para depurar erros comuns de conexão, como ECONNREFUSED, timeouts e exaustão de pool, e para otimizar o desempenho de aplicações MongoDB em diferentes cenários, como funções serverless, endpoints de API, aplicações de alto tráfego, tarefas de longa duração e concorrência. Seu funcionamento se baseia na análise de padrões de código e configuração, fornecendo recomendações específicas para ajustar parâmetros de conexão de acordo com o ambiente e a carga de trabalho, garantindo robustez e eficiência.

Funciona com~Claude Code~Codex CLI~Cursor
Part ofAgent Workflows
npx skills add https://github.com/mongodb/agent-skills/tree/main/skills/mongodb-connection

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Documentação

O que mongodb-connection faz?

Otimize a configuração de conexão do cliente MongoDB (pools, timeouts, padrões) para qualquer linguagem de driver suportada. Use esta skill ao trabalhar, atualizar ou revisar funções que instanciam ou configuram um cliente MongoDB (por exemplo, ao chamar connect()), ao configurar pools de conexão, ao solucionar erros de conexão (ECONNREFUSED, timeouts, exaustão de pool) ou ao otimizar problemas de desempenho relacionados a conexões. Isso inclui cenários como construir funções serverless com MongoDB, criar endpoints de API que usam MongoDB, otimizar aplicações MongoDB de alto tráfego, criar tarefas de longa duração e concorrência, ou depurar falhas relacionadas à conexão.

Individual skills in this repo

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

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-natural-language-querying

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.

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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