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qualityA Model Context Protocol server that provides web search capabilities using Google Custom Search API and webpage content extraction functionality.
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"Search for information about 'rag'" matching MCP servers:
- -securityFlicense-qualityAn MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.Last updated
- AsecurityAlicenseAqualityLocal RAG system for Claude Code with hybrid search (semantic + BM25), cross-encoder reranking, markdown-aware chunking, and 12 MCP tools. Zero external servers, pure ONNX in-process.Last updated1238MIT
- -securityAlicense-qualityA server that integrates Retrieval-Augmented Generation (RAG) with the Model Control Protocol (MCP) to provide web search capabilities and document analysis for AI assistants.Last updated4Apache 2.0
- -securityAlicense-qualityEnables AI assistants to search and retrieve information from your knowledge base using RAG (Retrieval-Augmented Generation) with hybrid search, document indexing, and ChromaDB vector storage.Last updated10MIT
- AsecurityAlicense-qualityA complete MCP server for Retrieval-Augmented Generation with file management and vector memory for agents. Supports multiple document formats (PDF, DOCX, TXT, MD, CSV, JSON) with semantic search using Hugging Face embeddings and ChromaDB for efficient vector storage.Last updated1181MIT
- -securityFlicense-qualityAn advanced MCP server providing RAG-enabled memory through a knowledge graph with vector search capabilities, enabling intelligent information storage, semantic retrieval, and document processing.Last updated3244
- -securityAlicense-qualityTurns Claude Desktop into a personal document question-answering system using local vector search. Index PDF, TXT, and Markdown documents into collections and get answers based strictly on your documents with zero hallucination.Last updated8MIT
- -securityFlicense-qualityProvides semantic search capabilities over glowfic.com content using a pre-built vector database and GTE-Large embeddings. It allows users to query specific continuities and authors through MCP-compatible tools in applications like Claude Code and Cursor.Last updated
- -securityFlicense-qualityAn agentic AI system that orchestrates multiple specialized AI tools to perform business analytics and knowledge retrieval, allowing users to analyze data and access business information through natural language queries.Last updated3
- -securityFlicense-qualityImplements a RAG workflow that integrates with any custom knowledge base and can be triggered directly from the Cursor IDE.Last updated
- AsecurityAlicense-quality"primitive" RAG-like web search model context protocol server that runs locally. ✨ no APIs ✨Last updated1121PythonMIT
- -securityFlicense-qualityConnects a RAG application to open-webui using Model Context Protocol (MCP), enabling server-to-client communication for context retrieval and tool usage in remote environments through Server-Sent Events (SSE).Last updated1
- AsecurityFlicenseAqualityProvides local Retrieval-Augmented Generation (RAG) capabilities using Ollama for embeddings and ChromaDB for vector storage. It enables users to ingest and perform semantic searches across PDF, Markdown, and TXT documents within MCP-compatible clients.Last updated4101
- -securityFlicense-qualityA Retrieval Augmented Generation system that enables AI assistants to perform semantic searches and manage document indices for markdown files. It supports PostgreSQL with pgvector and integrates both Google Gemini and Ollama for intelligent embedding generation.Last updated1
- -securityAlicense-qualityAn MCP-compatible system that handles large files (up to 200MB) with intelligent chunking and multi-format document support for advanced retrieval-augmented generation.Last updated9MIT
- -securityFlicense-qualityImplements Retrieval-Augmented Generation (RAG) using GroundX and OpenAI, allowing users to ingest documents and perform semantic searches with advanced context handling through Modern Context Processing (MCP).Last updated5
- -securityFlicense-qualityEnables RAG (Retrieval-Augmented Generation) capabilities with document processing, vector storage, and intelligent Q\&A using OpenAI embeddings and semantic search.Last updated
- AsecurityAlicense-qualityPrivacy-first local document search using semantic search. Runs entirely on your machine with no cloud services, supporting PDF, DOCX, TXT, and Markdown files.Last updated62,100222MIT
- AsecurityFlicense-qualityCombines a knowledge graph with RAG (Retrieval-Augmented Generation) capabilities for semantic code indexing and search. Enables creating entity relationships, managing observations, and performing semantic searches across indexed codebases.Last updated13
- -securityFlicense-qualityEnables semantic search across documents and code repositories using RAG (Retrieval-Augmented Generation) with vector embeddings. Automatically indexes PDF documents and performs relevance-scored lookups through ChromaDB and sentence transformers.Last updated
- AsecurityAlicense-qualityEnables creation and querying of knowledge bases using Google's Gemini API File Search feature, allowing AI applications to upload documents and retrieve information through RAG (Retrieval-Augmented Generation).Last updated316MIT
- AsecurityFlicense-qualityA server that implements Retrieval-Augmented Generation using GroundX and OpenAI, enabling semantic search and document retrieval with Modern Context Processing for enhanced context handling.Last updated3
- -securityAlicense-qualityEnables retrieval-augmented generation (RAG) by indexing and searching through documents (Markdown, text, PowerPoint, PDF) using vector embeddings with multilingual-e5-large model and PostgreSQL pgvector. Supports contextual chunk retrieval and incremental indexing for efficient document management.Last updated70MIT
- -securityAlicense-qualityA 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.Last updated1MIT
- -securityAlicense-qualityWeb crawling and RAG implementation that enables AI agents to scrape websites and perform semantic search over the crawled content, storing everything in Supabase for persistent knowledge retrieval.Last updated2,141MIT
- -securityFlicense-qualityEnables semantic search and question-answering over semiconductor component data stored in Excel files using ChromaDB vector database and HuggingFace language models through a RAG pipeline.Last updated
- -securityFlicense-qualityEnables storing and searching personal notes, documents, and snippets using semantic search and RAG capabilities across Claude Desktop, VS Code, and Open WebUI.Last updated1
- -securityFlicense-qualityProvides cocktail recommendations using a Retrieval-Augmented Generation (RAG) pipeline powered by LangChain, FAISS, and Groq. It enables users to search for cocktail recipes and receive personalized drink suggestions through natural language.Last updated
- -securityFlicense-qualityProvides hierarchical RAG over 299 Lenny Rachitsky podcast transcripts for product development brainstorming and insight retrieval. It enables semantic search across topics, insights, and examples to surface expert advice on product management and growth.Last updated5
- -securityFlicense-qualityEnables AI assistants to fetch, index, and perform semantic RAG-based searches on API documentation from various sources. It provides tools for hybrid search and collection management, allowing users to access up-to-date documentation from projects like Gemini and FastMCP.Last updated
- -securityFlicense-qualityEnables semantic search and question-answering over FAQ documents using RAG (Retrieval-Augmented Generation) with OpenAI embeddings and in-memory vector similarity.Last updated
- -securityFlicense-qualityEnables AI assistants to search and query PDF documents through a local RAG system with vector embeddings. Provides semantic document search capabilities while keeping all data stored locally without external dependencies.Last updated
- -securityAlicense-qualityTransforms static gemini-cli documentation into a queryable RAG service, enabling developers to ask questions about Gemini CLI in natural language and receive instant, accurate answers based on the official documentation directly within their workflow.Last updated8MIT
- -securityFlicense-qualityA pluggable and observable modular RAG framework that enables AI assistants to perform semantic search, document Q\&A, and knowledge base retrieval. It supports hybrid search, reranking, and multiple LLM backends through a standardized Model Context Protocol interface.Last updated8
- -securityAlicense-qualityA server that enables document searching using Vertex AI with Gemini grounding, improving search results by grounding responses in private data stored in Vertex AI Datastore.Last updated35Apache 2.0
- -securityAlicense-qualityProvides AI assistants with a standardized interface to interact with the Todo for AI task management system. It enables users to retrieve project tasks, create new entries, and submit completion feedback through natural language.Last updated4Apache 2.0
- -securityAlicense-qualityAn MCP server that provides comprehensive multimodal Retrieval-Augmented Generation (RAG) capabilities for processing and querying document directories, supporting text, images, tables, and equations.Last updated19MIT
- -securityFlicense-qualityEnables semantic search and question-answering over uploaded documents using vector embeddings and Google AI. Supports document organization with tags, section-aware queries, and hierarchical markdown structure preservation.Last updated
- -securityFlicense-qualityA Model Context Protocol server that exposes Retrieval-Augmented Generation capabilities and a weather tool, allowing clients to interact with document knowledge bases and retrieve weather information.Last updated
- -securityAlicense-qualityAn MCP server that enables RAG (Retrieval-Augmented Generation) on markdown documents by converting them to embedding vectors and performing vector search using DuckDB.Last updated4Apache 2.0
- AsecurityFlicense-qualityA TypeScript MCP server that allows querying documents using LLMs with context from locally stored repositories and text files through a RAG (Retrieval-Augmented Generation) system.Last updated417
- AsecurityFlicense-qualityEnables semantic search and contextual conversations with your Calibre ebook library using vector-based RAG technology. Supports project-based organization, multi-format book processing, and OCR capabilities for enhanced content extraction and retrieval.Last updated72
- -securityAlicense-qualityA lightweight server that provides persistent memory and context management for AI assistants using local vector storage and database, enabling efficient storage and retrieval of contextual information through semantic search and indexed retrieval.Last updated2MIT
- -securityFlicense-qualityEnables semantic search and retrieval-augmented generation (RAG) using Qdrant vector database. Supports indexing documents from URLs and local directories, with flexible embedding options using Ollama or OpenAI.Last updated2
- -securityAlicense-qualitySemantic code search for AI agents — hybrid vector + BM25 with cross-encoder reranking, AST-aware chunking for 14 languages, conversation memory, code annotations, and search analytics. Zero config, zero API keys. Just bunxLast updated2,89511Apache 2.0
- -securityAlicense-qualityProvides retrieval-augmented generation (RAG) capabilities by ingesting various document formats into a persistent ChromaDB vector store. It enables semantic search and retrieval using either OpenAI or Ollama embeddings for processing local files, directories, and URLs.Last updatedMIT
- -securityAlicense-qualityEnables advanced semantic search and document management by integrating the GroundX API with Claude Desktop and GitHub Copilot. It supports local file uploads, website crawling, and Google Drive synchronization for RAG workflows.Last updated1MIT
- -securityFlicense-qualityA modular Retrieval-Augmented Generation (RAG) framework that provides hybrid search and knowledge retrieval capabilities via the Model Context Protocol. It enables users to integrate document-based knowledge into LLM workflows with support for dense/sparse retrieval, reranking, and observability.Last updated