"General information about the web" matching MCP servers:

MCP server for semantic search over PDF documents using RAG, pgvector, and OpenAI embeddings. Includes a web app for document upload and management.

An MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.

An enterprise-ready MCP server that exposes a RAG tool for retrieving relevant context and metadata from a Qdrant vector database using natural language queries.

MCP Memory is a MCP Server that gives MCP Clients the ability to remember information about users across conversations using vector search.

An MCP server that integrates Crawl4AI with Supabase to enable AI agents to crawl websites, store content in a vector database, and perform RAG queries.

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.

A multi-agent Retrieval-Augmented Generation system exposed as an MCP server. Ask a question and a LangGraph pipeline plans the retrieval, pulls evidence from a pgvector knowledge base, optionally augments it with live web research, drafts a cited answer, and then self-critiques it for grounding — revising until the answer is supported by the sources.

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.

askDB is an MCP server that retrieves relevant database schema (DDL) from a Pinecone index and provides it to LLMs to write SQL, without connecting to the database itself.

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.

Enables answering Formula 1 FAQ questions via vector search and web search using Bright Data.

Integrates R2R (Retrieval-Augmented Generation) with Claude Desktop, enabling semantic search across knowledge bases and RAG-based question answering with support for vector, graph, web, and document search.

Enables asking natural language questions about local videos, using Whisper transcription, ChromaDB vector storage, and Groq for timestamped answers via MCP.

A local, fully-offline MCP memory server that enables persistent storage and retrieval of information using SQLite with both keyword and semantic vector search capabilities.

A server that enables vector and keyword search capabilities in Typesense databases through the Model Context Protocol, providing tools for collection management, document operations, and search functionality.

Provides persistent context management for AI agents by storing and querying semantic information using Upstash Vector DB and Google AI embeddings. It enables semantic search, batch operations, and metadata filtering to help agents retrieve relevant stored knowledge.

A fully-local, three-layer memory plugin for Claude Code with Chinese support, offering hybrid FTS5/trigram and HNSW vector search, self-iterating memory management, consolidation, and a web console.

An experimental MCP server that makes YouTube playlist transcripts available for AI assistants, enabling search and conversation about video content.

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

An MCP server that provides semantic memory on top of Qdrant vector search, with tools to store and retrieve information using embeddings.