"Information about RAG (Retrieval-Augmented Generation)" 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.

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

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.

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

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.

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.

AI building world

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 with Retrieval-Augmented Generation (RAG) for answering questions about imaginary SuperNova documentation. Enables semantic search over documentation using HuggingFace embeddings.

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.

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.