ViperGPT Visual Question Answering

ViperMCP server implementation by Ryan Sherby that provides visual question-answering capabilities through a mixture-of-experts approach based on the ViperGPT framework. The implementation combines multiple AI models including Grounding DINO, SegmentAnything, GPT-4o variants, X-VLM, MiDaS, and BERT to handle three core task areas: visual grounding, compositional image question answering, and external knowledge-dependent image question answering. Built as a FastMCP streamable-http server with Docker support and Smithery deployment options, it generates and executes Python code to solve complex visual reasoning tasks, making it valuable for applications requiring advanced image analysis, object detection with natural language queries, and multi-step visual reasoning workflows.