The Image Gen MCP Server is a general AI image generation service that provides cross - platform and multi - model image generation capabilities for various LLM chatbots through the Model Context Protocol (MCP) standard protocol. It supports multiple image models from OpenAI and Google, enabling seamless conversion from text conversations to visual content.

Outsource MCP is a unified interface service that supports multiple AI model providers. Through the MCP protocol, AI applications can conveniently call the text and image generation capabilities of different manufacturers.

An MCP server implementation for accessing Freepik's material resources and Mystic AI image generation capabilities

MCP Flux Studio is a powerful model context protocol server that integrates Flux's advanced image generation capabilities into AI programming assistants and supports Cursor and Windsurf IDEs.

The AI-Cursor-Scraping-Assistant is a web scraper generation tool based on Cursor AI and the MCP protocol. It can quickly analyze website structures and automatically generate Scrapy or Camoufox scraper code. The project consists of two major components: Cursor rule sets and MCP tool sets. It supports scraping e-commerce PLP/PDP pages and has anti-bot detection and bypass capabilities.

Replicate Flux MCP is an advanced server based on the Model Context Protocol (MCP), which uses the Flux Schnell and Recraft V3 SVG models through the Replicate API to provide high-quality image and vector graphics generation capabilities for AI assistants.

Minimax MCP Tools is an MCP server implementation that integrates Minimax AI capabilities, providing image generation and text - to - speech functions.

This is a prototype of a code execution server based on the MCP protocol, which combines the code generation capabilities of large language models with MCP tool integration, enabling AI agents to run Python code in an isolated sandbox and invoke external tools.

A server-side project based on the Model Context Protocol (MCP) that provides retrieval-augmented generation (RAG) capabilities. It can serve as a bridge between clients such as Cursor IDE and Claude Desktop and Contextual AI agents, enabling intelligent Q&A based on the knowledge base and context-aware responses.

This project implements an MCP server that integrates Venice AI's image - generation capabilities, providing an interactive image - generation service with an approval/regeneration workflow and supporting the generation of multi - view 3D views through the Gemini API.

MockLoop MCP is the world's first AI-native API testing platform integrating the Model Context Protocol (MCP). It revolutionizes API testing through AI-driven scenario generation, automated test execution, and intelligent analysis capabilities. The platform offers 5 AI prompts, 15 scenario resources, 16 testing tools, and 10 context tools, supports a dual-port architecture and stateful testing workflows, and is suitable for enterprise-level compliance and advanced analysis requirements.

This project tests the capabilities of LLM in information retrieval, API integration (such as IGN's APICARTO and GéoPlateforme services), and code generation through ChatGPT, including functions such as geocoding and postal code query, and explores the application of AI in technical evaluation and development assistance.

An MCP server that provides AI visual analysis capabilities, supporting functions such as web page screenshot capture, visual analysis, file operations, and report generation.

An MCP server that provides AI visual analysis capabilities, supporting functions such as web page screenshot capture, visual analysis, file operations, and report generation. Suitable for AI assistants such as Claude.

This project is an MCP server integrated with Stable Diffusion image generation functionality, providing image generation services for AI agents, including development and debugging tools and Goose platform integration capabilities.

A Google Gemini-based image generation and editing service that provides AI image processing capabilities through the MCP protocol

This project builds an AI system based on Nasdanika capabilities, focusing on operating on resource collections (interconnected models). It describes model elements and their relationships from multiple angles through the 'narrator' processor, and uses embeddings and vector storage to implement semantic search and RAG (Retrieval - Augmented Generation). It also supports the chat completion functions of OpenAI and Ollama.