MCP server that converts file contents into compact, line-numbered PNG images for vision models to read, reducing token usage by roughly 7x for large files.
MCP server that enables LLMs to understand images without native vision by converting image regions into text encodings (ASCII art, grayscale grids, color stats) and supporting progressive zoom, OCR, and overview summaries. Users can load images, get chunk overviews, crop and encode specific regions, and extract text using normalized coordinates.
Enables image analysis via OpenAI-compatible vision APIs, supporting local files, URLs, and base64 inputs with intelligent tiling for high-resolution images. Provides a secure, configurable MCP stdio server for structured vision analysis.
Enables text-only models to understand images through a conversational MCP server, supporting multi-turn follow-ups, URL inputs, and OpenAI-compatible vision APIs.