Search your personal memory layers by combining vector similarity with keyword matching to retrieve relevant episodic, semantic, and procedural memories.
AGPL 3.0
260,342 tools. Last updated 2026-07-05 05:20
"A vector database for efficient similarity search and AI applications" matching MCP tools:
- Retrieve the k nearest nodes to a query embedding using HNSW vector similarity. Use for semantic search after creating a vector index on a specific label and property.Apache 2.0
- Filter documents by metadata before ranking by vector similarity to enable production RAG and semantic search pipelines.MIT
- Set up an HNSW vector index for fast similarity search on graph nodes. Requires label, property, dimensions, and metric to index existing and future vectors.Apache 2.0
- Search Redis documentation and knowledge base to find information on concepts, data structures, features, and use cases including caching, session management, and semantic search.MIT
- Search for similar documents by vector similarity, with optional scalar filters like age > 25.Apache 2.0
Matching MCP Servers
- Flicense-qualityBmaintenanceA CLI-first semantic code search tool with MCP integration for AI assistants, enabling semantic search, AST-aware parsing, and code analysis across 13 languages.Last updated49
- Flicense-qualityDmaintenanceCombines Neo4j graph database with vector search using OpenAI embeddings for intelligent semantic search across knowledge graphs.Last updated3
Matching MCP Connectors
Search PubMed and summarize biomedical literature — designed for AI health agents.
Access comprehensive company data including financial records, ownership structures, and contact information. Search for businesses using domains, registration numbers, or LinkedIn profiles to streamline due diligence and lead generation. Retrieve historical financial performance and complex corporate group structures to support informed business analysis.
- Search Zvec vector stores by converting natural language queries into embeddings and retrieving similar items from a specified collection.Apache 2.0
- Find relevant content across namespaces using natural language queries or vector similarity. Filter results by metadata or keywords for precise discovery.Apache 2.0
- Generate vector embeddings from text for semantic search, RAG, clustering, or similarity tasks. Choose between query or document input type and adjust model quality and dimensionality.MIT
- Search memories by natural-language query with optional importance and similarity filters. Returns ranked results with similarity scores.MIT
- Generate dense embedding vectors from text for similarity search using OpenAI's embedding API.Apache 2.0
- Search persistent memory using semantic similarity to retrieve relevant memories and related knowledge graph facts.MIT
- Find past experiences that match your current task by searching episodic memory with vector similarity.MIT
- Create a vector index on a vector field in Baidu Vector Database to enable efficient similarity searches for AI applications.Apache 2.0
- Find similar vector embeddings in Zilliz Cloud collections using vector similarity search with optional filtering and result customization.Apache 2.0
- Resume a paused Zilliz Cloud vector database cluster to restore data access and processing capabilities for AI applications.Apache 2.0
- Search vector databases by combining semantic similarity with structured filters, then refine results using ranking strategies to retrieve relevant data.Apache 2.0
- Recall relevant memories from an AI agent's storage using hybrid search (vector + FTS5 + keyword).MIT
- Set up a Redis 8 vector similarity index using HNSW to perform approximate nearest neighbor search on hash-stored documents with float32 vectors.MIT
- Switch between databases in Baidu Cloud Vector Database to manage different datasets for vector search operations.Apache 2.0