Mongodb Search And AI

MongoDBOfficialPublished by MongoDB

Skill
Tool integration
Data and AI
Apache-2.0
Low risk
Has scripts

From the author

Covers everything about search in MongoDB: Atlas Search (full-text), Vector Search (semantic), and Hybrid Search. Use this skill for ANY question about MongoDB search, including open-ended and explanatory ones asked before the user has any data or use case — 'how does search work in MongoDB?', 'I'm new to MongoDB search, where do I start?', 'what's the difference between Atlas Search and Vector Search?', 'which search type should I use?', 'what is vector search?', 'how does automated embedding work?', 'can you explain hybrid search?'. Do not answer these from general knowledge; use this skill. Also use to build search features: full-text queries, autocomplete, fuzzy matching, faceted search, semantic similarity, embeddings, RAG, text containment or substring matching ('contains', 'includes'), case-insensitive or multi-field text search, and filtering across many fields. Provides workflows for choosing a search type, creating indexes, writing queries, and optimizing performance via the MongoDB MCP server.

— the description by MongoDB

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a comprehensive framework for integrating search capabilities into MongoDB applications. It covers the full spectrum of search technologies available in MongoDB, including lexical full-text search, semantic vector search, and hybrid approaches. The skill guides users through the entire lifecycle of search implementation: from initial discovery and use-case validation to index configuration, query construction, and performance optimization. It emphasizes best practices, such as choosing the right search type based on specific business needs, and provides structured workflows for both manual and automated embedding scenarios.

What it does

Helps users choose between Atlas Search, Vector Search, and Hybrid Search.
Provides workflows for creating and managing search indexes.
Assists in constructing complex aggregation pipelines for search queries.
Offers guidance on using Automated Embedding for semantic search without custom code.
Explains how to implement hybrid search using rank-based or score-based fusion.
Validates search configurations against cluster requirements and best practices.

When to use it

When you need to add full-text, semantic, or hybrid search to a MongoDB collection.
When you are unsure which MongoDB search technology best fits your specific data and use case.
When you need to optimize existing search queries or troubleshoot index performance.
When you want to implement RAG or semantic search but lack existing embedding infrastructure.
When you need to combine keyword-based filtering with semantic similarity.

Advantages

Prevents common anti-patterns like using inefficient regex or text operators.
Ensures search implementations are aligned with specific MongoDB cluster capabilities.
Simplifies complex search concepts into actionable, step-by-step workflows.
Provides clear guidance on model selection for automated embeddings.
Promotes performance-oriented index design and query construction.

Usage examples

“How do I implement semantic search for my product catalog?”: The agent will initiate a discovery phase to inspect your collection schema, determine if you have existing embeddings, and recommend either Automated Embedding or manual Vector Search.
“What is the difference between Atlas Search and Vector Search?”: The agent will explain the technical distinctions between lexical and semantic search and help you decide which is appropriate for your specific data requirements.
“I want to combine keyword filtering with vector similarity.”: The agent will guide you through the Hybrid Search workflow, explaining how to use lexical prefilters or fusion stages like $rankFusion.
“Help me get started with search.”: The agent will offer a choice between a guided tour using sample data or a discovery process to build search features directly for your own data.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/mongodb/mongodb-search-and-ai/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip mongodb-search-and-ai.zip -d .agents/skills

Documentation by MongoDB

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.

Core Principles

Understand before building - Validate the use case to ensure you recommend the right solution
Always inspect first - Check existing indexes and schema before making recommendations
Explain before executing - Describe what indexes will be created and require explicit approval
Handle read-only scenarios - Without create, update, or delete operation tools you are in read-only mode; hand the user index JSON to create themselves
Explain in accessible language - Describe technical concepts and map business requirements to technical implementations in terms the user can follow

Workflow

0. Check for a Quickstart Request

references/quick-start.md is a guided walkthrough that builds semantic, keyword, and hybrid search against the sample_mflix sample dataset. It is a teaching tour, not a way to build search for the user's own data.

Do not read references/quick-start.md until the user has chosen the tour. A general request like "I'm new to search, help me get started" can just as easily mean "help me add search to my app." Use AskUserQuestion:

"Two ways I can take this — which fits better?"

Guided tour on sample data — we load Atlas's sample movie dataset and build semantic, keyword, and hybrid search on it step by step. Best for learning how each type works.
Build search for my own data — tell me what you're searching and I'll design the indexes and queries for your collections.

Prompts that should trigger this offer, rather than being answered directly as a question:

"I'm new to MongoDB, how does search work?", "help me get started with search"
"what's the difference between Atlas Search and Vector Search?", "which search type should I use?"
"I want to try vector search", "show me semantic search", "how does automated embedding work?"
"I want to try keyword search", "show me full-text search", "I want to try hybrid search"

Skip the offer only when the intent is already unambiguous:

Explicit tour request ("give me a guided tour", "walk me through search using sample data") — load references/quick-start.md and start at its Step 0.
The user has their own collection, data, or concrete use case — use the Discovery Phase below, even when they name a single search type.

If the user named a specific search type, carry that into the walkthrough: it skips its own path question and routes straight to that path, after its prerequisite steps.

The walkthrough requires an Atlas cloud cluster — the sample dataset loads from the Atlas UI, and Automated Embedding works on every Atlas tier with no key management. Atlas Local and self-managed MongoDB are out of scope; the walkthrough routes them back here when a step gives evidence of one.

Otherwise, continue with the Discovery Phase.

1. Discovery Phase

Check the environment:

Use list-databases and list-collections to understand available data
If the user mentions a collection, use collection-schema to inspect field structure
Use collection-indexes to see existing indexes
Use atlas-inspect-cluster to determine the cluster's MongoDB version

Understand the use case: If the user's request is vague:

Ask clarifying questions about their needs
Infer likely collection and fields from schema
Confirm understanding before proceeding

Common questions to ask:

What are users searching for? (products, movies, documents, etc.)
What fields contain the searchable content?
Are they searching by free text, or by similarity to an existing item (e.g. "given movie A, find similar movies")?
Do they need exact matching, fuzzy matching, or semantic similarity?
Do they need filters (price ranges, categories, dates)?
Do they need autocomplete/typeahead functionality?
Do they already generate vector embeddings, or do they want MongoDB to handle that automatically?

2. Determine Search Type and Consult the Reference File

Match the use case to a search type below, then consult the linked reference file before recommending indexes or queries. Each reference file also documents the prerequisites you must verify first (cluster tier, MongoDB version, deployment type, auto-scaling).

Atlas Search (Lexical/Full-Text): Use when users need:

Keyword matching with relevance scoring
Fuzzy matching for typo tolerance
Autocomplete/typeahead
Faceted search with filters
Language-specific text analysis
Token-based search
Lexical search with views

→ Consult both references/lexical-search-indexing.md (index) and references/lexical-search-querying.md (query).

Automated Embedding (Semantic search, no embedding code): Use when users need:

Semantic / vector search without writing embedding code
No existing vector pipeline or embedding infrastructure
Quick setup: MongoDB auto-generates and manages embeddings using Voyage AI models
Text data already stored in Atlas that they want to search by meaning
RAG or AI agent memory with minimal setup

→ Consult references/automated-embedding.md.

Vector Search (Semantic, bring your own embeddings): Use when users need:

Semantic similarity with their own pre-generated embeddings
A specific embedding model not provided by Voyage AI
Image, audio, or multimodal embeddings (Automated Embedding is text-only)
Self-managed MongoDB without Voyage AI API key configured
Vector search with views

→ Consult references/vector-search.md.

Hybrid Search: Use when users need:

Combining multiple search approaches (e.g., vector + lexical, multiple text searches)
Queries like "find action movies similar to 'epic space battles'" (combining keyword filtering with semantic similarity)
Results that factor in multiple relevance criteria
Uses $rankFusion (rank-based) or $scoreFusion (score-based) to merge pipelines

→ Consult references/hybrid-search.md, plus the lexical/vector files for the individual pipeline stages.

3. Execution and Validation

Creating indexes:

Explain the index configuration in plain language
Show the JSON structure
Ask what the user wants to name the index
Get explicit approval: "Should I create this index?"
Use MCP's create-index tool after approval
In read-only mode, provide the complete index JSON for creation via the Atlas UI

Running queries:

Show the aggregation pipeline
Execute using MCP's aggregate tool
Present results clearly

Refining existing queries:

Ask the user to share their current query
Compare against the query patterns and best practices in the relevant reference file(s)
Propose specific improvements with before/after examples
Run the revised query with aggregate to validate the results

Anti-Patterns to Avoid

NEVER recommend $regex or $text for search use cases. Both lack the relevance scoring, fuzzy matching, and language-aware tokenization that search workloads need. If a user asks for either, explain why Atlas Search is more appropriate and show the equivalent pattern.

Handling Edge Cases

Fields you can't find — inspect available fields with collection-schema, then confirm the intended field with the user
A required field doesn't exist — explain what to add and how (e.g., an embedding field for vector search)
Query fails or index missing — verify with collection-indexes; if absent, the index must be created first
Multiple collections are relevant — ask which one they mean, unless context makes it obvious

Files

ATTRIBUTION.mdAdded by AutoGineer845 B
LICENSELicence file added by AutoGineer11.1 KiB
SKILL.md8.7 KiB
references/automated-embedding.md15.9 KiB
references/hybrid-search.md23.1 KiB
references/lexical-search-indexing.md19.5 KiB
references/lexical-search-querying.md17.5 KiB
references/quick-start.md45.1 KiB
references/vector-search.md18.7 KiB
scripts/quickstart_complete.pyScript13.6 KiB

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Requirements and compatibility

Written for: Claude Code

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Names Claude Code tools, Needs an API key, Needs Python

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Attribution and licence

Published by

MongoDB

Publisher

MongoDB (Official)

Source

mongodb/agent-skills · skills/mongodb-search-and-ai · commit 155bb9c

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-09

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