"Using Latest Modern C++ Features in Programming" matching MCP servers:

Enables natural language search and analysis of uploaded PDF, CSV, and Excel documents using retrieval-augmented generation and MCP tools, providing contextual answers to user queries.

An MCP server that provides AI assistants with access to Multi Theft Auto: San Andreas function documentation through vector similarity search and smart keyword expansion. It enables efficient information retrieval with features like deprecation warnings and SQLite caching for technical documentation.

MCP server for semantic search in an Obsidian Second Brain vault using self-hosted Qdrant and Google Gemini embeddings.

Enables AI agents to manage and search data in Redis using natural language. Supports hashes, lists, sets, sorted sets, streams, JSON, and vector search.

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 AI assistants to perform semantic search and retrieval across codebases stored in Qdrant, using Ollama models for GPU-accelerated embeddings.

Enhances large language models with competitive programming knowledge by leveraging OI-Wiki content through vector search, allowing models to retrieve relevant algorithms and techniques.

A server that implements Retrieval-Augmented Generation using GroundX and OpenAI, enabling semantic search and document retrieval with Modern Context Processing for enhanced context handling.

Model Context Protocol (MCP) server for TigerGraph that lets AI agents interact with TigerGraph through the MCP standard using pyTigerGraph's async APIs.

Provides persistent, semantic memory storage for LLMs across sessions using vector embeddings and FAISS search. Features bio-inspired memory consolidation, intelligent forgetting, and semantic retrieval without API costs.

MCP server for compressing AI embeddings by 5-7x using TurboQuant (PolarQuant + QJL), with tools to compress, decompress, estimate savings, and embed+compress vectors.

A high-performance MCP server for semantic search and codebase indexing using the Qdrant vector database. It features optimized embedding pipelines, AST-aware chunking, and git metadata enrichment for fast, privacy-focused local or remote search.

A Python server that enables retrieval-augmented generation through semantic, question/answer, and style search modalities using PostgreSQL and pgvector for embedding storage and retrieval.

An MCP server that gives AI assistants the ability to remember user information (preferences, behaviors) across conversations using vector search technology.

A server component of the Model Context Protocol that provides intelligent analysis of codebases using vector search and machine learning to understand code patterns, architectural decisions, and documentation.

RAGX MCP Server enables retrieval-augmented generation with document ingestion, hybrid search, and agentic answering using Claude, exposing tools for querying, searching, and managing documents.

Enables storing, semantically searching, listing, and deleting text records in any Qdrant collection using plain vectors compatible with default LangChain and n8n setups.

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.

Enables MCP clients to remember user preferences and behaviors across conversations using vector search technology.

Provides a comprehensive Model Context Protocol interface for RAGFlow, enabling AI models to perform semantic retrieval, manage datasets, and handle document chunks. It supports advanced features like GraphRAG and RAPTOR for sophisticated knowledge base management and natural language querying.