Mongodb Search And Ai
Skill Verified ActiveGuides 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.
To empower MongoDB users to build powerful search functionalities, from basic text matching to advanced semantic similarity and hybrid approaches, by providing clear guidance and workflows.
Features
- Guides implementation of Atlas Search, Vector Search, and Hybrid Search
- Recommends appropriate search type based on use case
- Provides workflows for index creation and query construction
- Includes detailed reference files for lexical, vector, and hybrid search
- Offers guidance on version checks and query optimization
Use Cases
- When users need to build text-based search functionality (autocomplete, fuzzy matching)
- When users need semantic similarity search using embeddings or RAG
- When users need to combine lexical and vector search for hybrid approaches
- When users need to understand and implement Atlas Search indexes and queries
Non-Goals
- Implementing general database administration tasks
- Replacing the MongoDB Atlas UI for index management (provides JSON for manual creation)
- Performing arbitrary database queries outside of search and indexing
Installation
First, add the marketplace
/plugin marketplace add mongodb/agent-skills/plugin install agent-skills@mongodb-pluginsQuality Score
VerifiedTrust Signals
Similar Extensions
MongoDB Connection Optimizer
100Optimize 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.
Agentdb Query
99Query AgentDB through the controller bridge -- semantic routing, hierarchical recall, causal graphs, context synthesis, pattern store/search
V3 Memory Unification
99Unify 6+ memory systems into AgentDB with HNSW indexing for 150x-12,500x search improvements. Implements ADR-006 (Unified Memory Service) and ADR-009 (Hybrid Memory Backend).
Embeddings
99Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.
Azure AI Search SDK for Python
95Azure AI Search SDK for Python. Use for vector search, hybrid search, semantic ranking, indexing, and skillsets. Triggers: "azure-search-documents", "SearchClient", "SearchIndexClient", "vector search", "hybrid search", "semantic search".
Migrate Validate
100Validate pending migrations for foreign key consistency, rollback safety, and best practices