"Understanding RAG (Retrieval-Augmented Generation or related topics)" matching MCP servers:

Enhances AI model capabilities with structured, retrieval-augmented thinking processes that enable dynamic thought chains, parallel exploration paths, and recursive refinement cycles for improved reasoning.

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.

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

A black-box flight recorder for RAG retrieval inside MCP agents. Logs what chunks the model saw, scores, sources, and rankings - so you can audit, replay, and diff retrieval runs after the fact.

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

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

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

Serves legal retrieval-augmented generation over Brazilian federal legislation, returning exact statutory text with URN-LEX and Planalto URLs to prevent hallucination, running locally with optional GPU acceleration.

Provides 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.

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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.

An MCP server that indexes documents and serves relevant context to LLMs via Retrieval Augmented Generation (RAG).

Enables 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.

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.

Enables retrieval-augmented generation over a local markdown corpus, allowing grounded, cited answers via an MCP tool or CLI.

A Model Context Protocol (MCP) server for Retrieval-Augmented Generation (RAG) operations. It provides tools for building and querying vector-based knowledge bases from document collections, enabling semantic search and document retrieval capabilities.

Enables post-hoc auditing of an agent's retrieval supply chain by recording digest-only provenance from source through chunk, transformation, context and decision, then reporting influence rankings with an honest unexplained residual, lineage traces, advisory source trust and retirement recommendations, and retrieval schema drift. It never performs retrieval, ranks documents at query time, or blocks decisions.

An evaluation harness that probes MCP-based retrieval servers for calibration, relevance, coverage, citation integrity, and more, also exposing the probes as MCP tools for assistants and CI agents.

Enables RAG (Retrieval-Augmented Generation) capabilities with document processing, vector storage, and intelligent Q\&A using OpenAI embeddings and semantic search.

An MCP-compatible system that handles large files (up to 200MB) with intelligent chunking and multi-format document support for advanced retrieval-augmented generation.

Enables local retrieval-augmented generation document search for Cline by providing semantic search over indexed files, listing indexed sources, and reporting index status through MCP tools.

A Retrieval Augmented Generation MCP server that ingests documents into a local vector database and enables semantic search queries.

An MCP-based multi-agent retrieval-augmented generation system that enables question answering over academic papers with hybrid search, knowledge graph multi-hop reasoning, and source-cited answers.

Connects RAG knowledge bases to Claude Desktop and Claude Code, allowing retrieval-augmented generation from ingested documents.

Implements 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).

Local 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.

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.

Enables local-first hybrid knowledge retrieval from authorized Markdown and plain-text files, combining full-text and vector search with reranking and traceable source references via a single search tool.

Minimal RAG-over-a-corpus MCP retrieval: search_knowledge returns cited chunks. Local embeddings.

Provides retrieval and analysis tools for a quantitative-finance research library, enabling whole-passage answers with verifiable citations, citation checking, entity graph exploration, and corpus inspection.