"Automated Chat Service Based on Personalized Text Messaging Style" matching MCP servers:

A conversational chat interface that uses RAG over a synthetic test corpus and a domain-knowledge corpus, exposing retrieval via MCP.

Provides semantic memory and persistent storage for Claude, leveraging ChromaDB and sentence transformers for enhanced search and retrieval capabilities.

Provides intelligent, persistent memory for AI assistants with semantic search, natural language queries, and OAuth-based team collaboration, enabling context-aware conversations across multiple clients.

Facilitates semantic analysis of chat conversations through vector embeddings and knowledge graphs, offering tools for semantic search, concept extraction, and conversation pattern analysis.

Enables character management and semantic search for the Auto-Movie application through WebSocket communication. Supports creating characters with personality/appearance descriptions and finding similar characters using natural language queries with embedding-based similarity matching.

This MCP server provides semantic document search and retrieval, enabling AI assistants to search documents, search categories, and retrieve category hierarchies using the Model Context Protocol.

A clean architecture backend API that registers and semantically searches data using vector embeddings, acting as an MCP context provider to connect AI models to external data.

A lightweight RAG system that provides an MCP server for searching and interacting with vector-based knowledge bases. It enables users to perform retrieval-augmented generation and search across Qdrant collections through a standardized interface.

A cloud-based vector memory service that provides AI assistants with persistent storage, semantic search, and entity management via the Model Context Protocol. It features multi-tenant isolation and bidirectional synchronization with macOS and Google contacts and calendars.

MCP server that enables searching a TOTVS Protheus ERP knowledge base using vector similarity and reranking.

Enables semantic search over your Cursor IDE chat history by vectorizing prompts and storing them in LanceDB. Provides a Dockerized API to perform vector similarity searches against your chat history.

Enables document Q&A and knowledge retrieval through hybrid semantic and keyword search, with tools for document ingestion, chunking, summarization, PII redaction, and RAGAS-based evaluation.

Enables AI-powered document analysis and querying for project documentation using vector embeddings stored in Redis. Supports document upload, context-aware Q\&A, automatic test case generation, and requirements traceability through OpenAI integration.

Enables semantic search across text documents using vector embeddings stored in PostgreSQL. Provides multiple search modalities including semantic similarity, question/answer, and style-based search through a retrieval-augmented generation system.

Enables Claude Desktop to search private documents using Azure AI Search and perform web searches with Bing, providing AI-enhanced results with source citations through Azure AI Agent Service or direct Azure AI Search integration.

Provides AI assistants with long-term semantic memory capabilities through local vector-based storage. Enables storing, recalling, and managing information across sessions with complete privacy using ChromaDB, with no data ever leaving your machine.

Enables AI agents to interact with an embedded graph database (GrafeoDB) via the Model Context Protocol, providing tools for graph CRUD, GQL queries, full-text and vector search, and graph algorithms.

Semantic memory for AI builders: capture the tacit engineering know-how that never reaches your docs, recall it the moment it applies. Built in Rust on Postgres and pgvector.

A local MCP server that provides semantic code search for Python codebases using tree-sitter for chunking and LanceDB for vector storage. It enables natural language queries to find relevant code snippets based on meaning rather than just text matching.

Enables LLM hosts to retrieve live, relevant documentation excerpts from official library docs sites via a search-and-RAG tool, avoiding reliance on training data.