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…
- installs 8w
- 1,550
- 30-day movement
- starts with the next reading
- Related entries
- 1
- Connections
- 0
An agent skill that guides an AI coding agent through building MongoDB Atlas Search (lexical), Vector Search (semantic), and Hybrid Search solutions. It prescribes a workflow of inspecting the database, choosing a search type from reference files, then creating and validating indexes and queries via the MongoDB MCP server.
You want your agent to pick the right MongoDB search approach and write correct indexes and queries without resorting to $regex or $text anti-patterns.
Use it to
- Build autocomplete, fuzzy matching, or faceted full-text search
- Implement semantic similarity search and RAG with embeddings
- Combine lexical and semantic hybrid search
- Add case-insensitive or multi-field text search and filtering
- Optimize and troubleshoot existing Atlas Search indexes
For MongoDB users building search or RAG features with an AI agent
- Host repository
- mongodb/agent-skills
- Version
- 1.0.0
- Installs, lifetime
- 4,100
- Installs, 8 weeks
- 1,550
- Licence
- Apache-2.0
- Host stars
- 183
- Host language
- TypeScript