"Understanding RAG (Retrieval-Augmented Generation or related topics)" 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 query OpenRouter model information including prices, ELO rankings, context, and perform comparisons.

MCP server for multimodal understanding and object grounding (bounding boxes) across images, videos, and documents, with support for multiple AI providers (Zhipu GLM-V, OpenAI GPT-4o, Anthropic Claude, or any OpenAI-compatible endpoint).

MCP server that provides OpenRouter model pricing data, enabling price lookups, trending/cheapest lists, and model searches without an API key.

Enables Claude Desktop, Claude Code, and autonomous AI agents to route messages into Telegram supergroup forum threads and manage topics by creating, editing, closing, reopening, and listing them with tag-to-thread mapping.

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

A zero-dependency Node.js playground for debugging how MCP clients authenticate to an MCP server over HTTP.

Integrates Xiaomi MiMo's multimodal API to enable understanding of images, audio, and video through natural language prompts.

Provides live wait times and office details for all 60 Oregon DMV field offices by scraping the ODOT website, with no authentication required.

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 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 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 coding agents to retrieve project context from an AnythingLLM RAG at session start and write back human-approved durable facts via five workflow-shaped tools.

Enables querying LlamaCloud document indices using retrieval-augmented generation, providing intelligent answers with sources directly in Cursor IDE.

Enables creation and querying of knowledge bases using Google's Gemini API File Search feature, allowing AI applications to upload documents and retrieve information through RAG (Retrieval-Augmented Generation).

Enables AI-powered image generation using Azure DALL-E 3 and FLUX models with intelligent automatic model selection. Generates stunning photorealistic or creative images directly within LibreChat through simple text prompts.

Enables image and video generation using BigModel AI's CogView and CogVideoX models via MCP, supporting batch image generation and various configuration options.

A Model Context Protocol server that analyzes webpage design structures, providing detailed layout, navigation, content, form, image, and link information from a given URL.

Provides image generation capabilities for Claude using the Replicate Flux model, allowing users to create images from text prompts with customizable parameters like aspect ratio and output format.

A modular RAG service framework that exposes tools like query_knowledge_hub, list_collections, and get_document_summary via MCP protocol, enabling AI assistants to perform retrieval-augmented generation.

Enables document ingestion, semantic search, and retrieval-augmented generation via MCP tools and REST API, using vector embeddings and intelligent chunking.

Enables MCP-capable agents to generate and edit images through Gemini or OpenAI, returning an absolute file path instead of image bytes to keep context windows clean.

An agentic Retrieval-Augmented Generation (RAG) system that combines a small curated machine learning knowledge base with real-time web search capabilities, powered by the Model Context Protocol (MCP).

Enables retrieval-augmented generation search and chat over documents stored in Azure Blob Storage, with a Streamlit app for uploading files and interacting via a LangGraph agent.

Exposes a RAG agent built with LangGraph, enabling retrieval-augmented question answering over a PostgreSQL/pgvector corpus via the MCP protocol.

Enables semantic search and question-answering over FAQ documents using RAG (Retrieval-Augmented Generation) with OpenAI embeddings and in-memory vector similarity.

Exposes a Retrieval-Augmented Generation pipeline as MCP tools, allowing users to index documents and query them through any MCP-compatible client like Claude or IDEs.

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).

A RAG MCP server that enables retrieval-augmented question answering over PyTorch documentation, with hybrid search (FAISS+Chroma, BM25) and source-cited responses from DeepSeek.

MCP server that integrates Apache Ranger authorization with RAG, enforcing Ranger policies to control access to knowledge bases before forwarding queries to RAG Studio.