Adds vision capabilities to text-only LLMs by integrating external vision models via MCP. It supports OCR, error screenshot reading, UI description, image comparison, and natural-language queries on images.
Bridges vision models to text-only coding models using Florence-2, enabling non-vision LLMs to describe images, extract text, and analyze screenshots via MCP tools.
Enables text-only reasoning models to see images by wrapping vision-language models as MCP tools, supporting image description, OCR, chart analysis, and custom questioning within MCP-compatible IDEs.
Provides vision understanding capabilities such as image analysis, OCR, object localization, and video frame analysis, plus optional image generation and editing, to coding agents via OpenAI-compatible multimodal models. Runs as a local MCP server with HTTP and stdio transports, configurable for clients like Codex, Claude Code, Kimi, and Cursor.
