"Information on RAG Documents or Processing" matching MCP servers:

Enables AI agents to query OpenRouter model information including prices, ELO rankings, context, and perform comparisons.

An MCP server that enables AI assistants to create and run Processing sketches directly through natural language commands.

Enables processing, validation, optimization, and analysis of 3D models with glTF/GLB support, including format conversion, compression (Meshopt/Draco), texture optimization, and detailed model statistics.

A unified MCP server for document processing that enables creating, editing, and converting Word documents (DOCX), PDFs, Markdown, and images, with support for templates, formatting, and batch operations.

A template for deploying an MCP server on Vercel, with example tools like roll_dice and get_weather.

A humorous MCP server that always advises against shipping on Friday, returning a catastrophic risk level and a rotating reason.

MCP server deployed on EdgeOne Pages, providing a Streamable HTTP MCP server for intelligent chat applications with tool capabilities like generating webpages.

A comprehensive Model Context Protocol (MCP) server implementing the latest MCP specification with tools, resources, prompts, and enhanced sampling capabilities that features HackerNews and GitHub API integrations for AI-powered analysis.

A TypeScript MCP server that allows querying documents using LLMs with context from locally stored repositories and text files through a RAG (Retrieval-Augmented Generation) system.

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.

This read-only MCP Server allows you to connect to XML Documents data from Claude Desktop through CData JDBC Drivers. For full CRUD support, check out our MCP Server for XML Documents (https://www.cdata.com/drivers/xml/download/mcp).

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.

A deployment template for running Model Context Protocol servers as Vercel Functions. It provides a foundation for developers to build and host custom tools, prompts, and resources with optimized execution for MCP client integration.

Enables LLM agents to efficiently understand and navigate a codebase by providing semantic search over symbols and a reference graph, replacing expensive grep/glob calls with structured tools like definition lookup, caller/callee queries, and change-impact analysis.

Provides comprehensive system diagnostics and hardware analysis through 10 specialized tools for troubleshooting and environment monitoring. Offers targeted information gathering for CPU, memory, network, storage, processes, and security analysis across Windows, macOS, and Linux platforms.

Enables AI agents to read documents in Excel, DOCX, PDF, and TXT formats via MCP protocol.

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

A simple MCP server implementation in TypeScript that communicates over stdio, allowing users to ask questions that end with 'yes or no' to trigger the MCP tool in Cursor.

A powerful Model Context Protocol server that creates intelligent graph representations of your codebase with comprehensive semantic analysis capabilities, supporting 11 languages and 26 MCP methods.

Transforms static gemini-cli documentation into a queryable RAG service, enabling developers to ask questions about Gemini CLI in natural language and receive instant, accurate answers based on the official documentation directly within their workflow.