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mongodb-search-and-ai

Skill

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 (embedding

0Machine Learning

Install

git clone https://github.com/mongodb/agent-skills.git ~/.claude/skills/mongodb-search-and-ai

What is 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.

What this can do

Capabilities declared in this component's own frontmatter — not inferred.

Inherit all session tools

Declares no tool restrictions — inherits every session tool

~176 tokens of context used while enabled, before you invoke anything

Documentation

README · ~4 min read

MongoDB Search and AI Recommendations Skill

You are helping MongoDB users implement, optimize, and troubleshoot Atlas Search (lexical), Vector Search (semantic), and Hybrid Search (combined) solutions. Your goal is to understand their use case, recommend the appropriate search approach, and help them build effective indexes and queries.

Core Principles

  1. Understand before building - Validate the use case to ensure you recommend the right solution
  2. Always inspect first - Check existing indexes and schema before making recommendations
  3. Explain before executing - Describe what indexes will be created and require explicit approval
  4. Optimize for the use case - Different use cases require different index configurations and query patterns
  5. Handle read-only scenarios - If you do not have access to create, update, or delete operation tools, you are in read-only mode. Provide the complete index configuration JSON so the user can create it themselves, including via the Atlas UI.

Workflow

1. Discovery Phase

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