"RAG (Retrieval-Augmented Generation) system that can store papers and web pages" matching MCP servers:

Enables retrieval-augmented generation by embedding queries with a chosen provider (e.g., OpenAI) and searching supported vector stores (Pinecone, pgvector) to return relevant content.

Enables AI agents to fetch, crawl, extract, and search web content through a unified set of tools, with automatic routing to specialized backends.

MCP server for a modular RAG system that enables natural language question answering over enterprise documents with intent-aware routing, adaptive retrieval, and citation-backed responses.

An MCP server that retrieves relevant PDF chunks via local embeddings and returns them to IDE agents (Cursor, Kiro, Claude Code) for answer generation.

An open-source MCP server providing AI agents with neural web search via Exa and tiered web fetch (Exa, local browser, Firecrawl) as a drop-in replacement for built-in web tools, preserving provenance and guarding against SSRF.

Enables AI agents to parse PDFs and search local document context through multimodal extraction and vector-based retrieval, all exposed as MCP tools.

Enables MCP-capable agents to perform reliable web search and content fetching through a single server that automatically fails over across multiple providers. It handles keyed or keyless authentication automatically and returns clean Markdown or text results.

Enables AI coding assistants to query private academic paper collections via standard MCP tools, with hybrid retrieval, reranking, and inline citations.

Enables LLMs to interact with vehicle CAN bus and OBD-II data through a simulated ECU environment. Provides tools for reading frames, decoding messages via DBC files, monitoring signals, and querying automotive diagnostics without requiring physical hardware.

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

Enables AI applications to access and contextualize organizational knowledge sources including GitHub repositories and internal documentation through standardized MCP protocol integration. Features OAuth 2.1 authentication, vector-based semantic search, and optimized context chunking for enterprise development workflows.

Enables deterministic auditing of RAG retrieval faithfulness and hallucination scores, providing structured JSON output for AI agents and MCP-compliant clients.

Monitors PubMed and medRxiv for configurable research topics, summarizes papers using a local LLM, and stores deduplicated results in a local database.

Exposes two MCP tools (discover and execute) that enable agents to query an OpenAPI schema via natural language and execute matched API operations.

MCP server for scientific grounding: search and discover open-access research papers across arXiv, OpenAlex, Crossref, PubMed, and Semantic Scholar, and retrieve references/citations from paywalled journals via public DOI/abstract metadata.

Enables semantic search across scientific papers in your Zotero library with hybrid search, incremental indexing, and cross-encoder reranking.

A server that integrates Retrieval-Augmented Generation (RAG) with the Model Control Protocol (MCP) to provide web search capabilities and document analysis for AI assistants.

A 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.

Single MCP server that manages multiple personal collections (todos, bookmarks, etc.) with only six generic tools, driven by a YAML registry for validation.

Unified academic paper search MCP server integrating OpenAlex, Semantic Scholar, CrossRef, and arXiv APIs, enabling Claude to search and analyze over 500 million scholarly papers.