"Information about RAG" matching MCP servers:

MCP server for Google's Gemini File Search (RAG). Manage file search stores, upload documents, and query with RAG through 12 tools.

Enables document-based Q&A with multi-modal RAG, hybrid retrieval, knowledge graph reasoning, and multi-agent orchestration via MCP tools.

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

Enables AI assistants to perform retrieval-augmented generation with hybrid search, reranking, multi-modal image processing, and RAG evaluation through standardized MCP tools.

Enables MCP clients to query a modular RAG knowledge hub with hybrid dense/sparse retrieval, reranking, multimodal image support, and traceable observability, all through natural language tools.

Enables AI clients to retrieve study-abroad admissions knowledge through a local, pluggable RAG pipeline with hybrid search, reranking, citations, and observability. It provides MCP tools, document ingestion, and a Streamlit dashboard for managing and evaluating retrieval.

Enables creating Manim animations via natural language commands using DeepSeek API, with a RAG system for Manim documentation retrieval.

The MCP server provides tools for entity extraction, query refinement, and relevance checking to enhance RAG applications by integrating with OpenAI and Gemini.

Provides current weather information for any location and generates AI-powered responses using Google Gemini through FastMCP.

A lightweight RAG server deployed on Azure Container Apps that ingests PDFs, creates embeddings, and exposes a search_documents tool over MCP for retrieving relevant document chunks.

Generates RAG pipeline configurations from natural language descriptions, providing recommendations for chunking, embeddings, and vector stores.

Enables agents to get fast, calibrated probabilistic answers from Jev (Typesafe AI) to yes/no, scale, or choice questions about provided material, without using a generative model.

An MCP server that protects AI agents and RAG systems from adversarial poisoning attacks like prompt injection, steganography, and data anomalies through multi-layer sanitization, entropy-based detection, and consensus verification.

Exposes the four Ejentum cognitive harnesses (reasoning, code, anti-deception, memory) as MCP tools any agentic client can call. Drop-in scaffolding that catches LLM failure modes like sycophancy, hallucination, and reasoning shortcuts.

Verifies factual claims against live sources and returns a verdict, confidence score, and citations for any agent to use before stating uncertain facts.

Generate and edit images with GPT Image 2.5 for $0.029 each, inside Claude, Cursor or any MCP client. A free you.bot API key comes with 50 credits (about 17 images) and needs no card.

An MCP server that lets any AI agent evaluate RAG outputs -- faithfulness scoring, hallucination detection, and retrieval quality metrics -- with zero API keys, using MCP sampling.

Enables AI assistants to rewrite AI-styled English prose into more natural human writing, with tools for humanizing text and retrieving information about the underlying model.

Enables coding agents to request calibrated probability judgments over typed questions—yes/no, named choices, or ordered scores—about a described situation, returning one probability per option rather than free text. Runs Cloudflare's Clef model locally on Apple Silicon Macs, with images or video frames optionally attached, and answers delivered as a single MCP tool call.

Provides AI agents with structured knowledge about 67+ generative AI models, including model recommendations, prompt formatting, parameter guidance, and validation. It acts as a prompt engineering co-pilot that helps agents use their existing tools more effectively.