"H&M" matching MCP servers:

Enables AI agents to use a neuro-symbolic memory fabric with bi-temporal knowledge graph and holographic VSA, providing tools for adding, searching, temporal queries, auditing, and proving memories with cryptographic provenance and zero-LLM ingest.

MCP server for OpenAI Vector Store API, managing vector stores, files, file batches, and semantic search.

Enables AI agents to query a local knowledge graph built from document collections using hybrid search (BM25 + vector fusion) and entity-relationship extraction. Supports privacy-first, offline operation with tools for semantic search, entity graph exploration, and corpus statistics.

A multimodal local memory MCP server that lets AI agents search across text, PDFs, images, audio, and video using Gemini Embedding 2 and a local ChromaDB.

Connects AI assistants to a persistent memory engine with Neo4j knowledge graph and ProMem extraction, enabling long-term context and associative memory across chats and workspaces.

Enables semantic search over local markdown note collections using vector embeddings, with real-time file watching and zero-config setup.

Enables Claude to store and query personal finance transactions using semantic search. Transactions are persisted in ChromaDB and JSON, allowing natural language questions about spending trends and portfolio allocations.

Enables Claude to search and retrieve documents from Azure AI Search indexes with intelligent summarization and analysis using LangGraph workflows and optional Google Gemini integration.

A Model Context Protocol (MCP) server that enables semantic search and retrieval of documentation using a vector database (Qdrant). This server allows you to add documentation from URLs or local files and then search through them using natural language queries.

An Elasticsearch-based AI memory system optimized for Chinese that enables persistent knowledge storage and complex entity relationship management via the Model Context Protocol. It features advanced semantic search using the IK analyzer and supports multi-zone memory isolation for specialized knowledge graphs.

Enables reading and writing to a Pinecone vector index, including semantic search, document management, and stats retrieval.

MCP server for a self-hosted RAG system that enables AI tools to search and retrieve grounded answers from locally ingested documents via MCP tools, with local embeddings and no API key required.

A local Mem0 MCP server for macOS with a menu bar toggle, storing memories locally in Chroma and using any OpenAI-compatible LLM for fact extraction.

An MCP server for intelligent log analysis providing semantic search, error pattern clustering, and smart error detection. It enables users to process, vectorize, and query local logs to efficiently identify issues and generate AI-powered summaries.

A Docker-based local RAG backend that provides advanced document search capabilities using vector, graph, and full-text retrieval via the Model Context Protocol. It supports over 28 file formats and tracks evolving relationships between concepts using a Neo4j-backed graphiti implementation.

A fully local, offline Chinese-optimized MCP memory server using SQLite and local embeddings, enabling MCP clients to store, semantically search, recall, and count memories without any API calls.

Enables interaction with VikingDB, a high-performance vector database, for storing and searching vector data via natural language.

Enables AI assistants to crawl websites, extract and store web content with semantic search capabilities using vector embeddings, and retrieve information through natural language queries with tag-based filtering and intelligent content cleaning.

Enables users to ingest PDF/DOCX/TXT/MD documents and ask natural language questions about them, using local embeddings and Groq-powered retrieval-augmented generation.

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