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.
MCP server that reduces AI agent token usage by up to 90% through intelligent context compression. Enables efficient code exploration, multi-file refactoring, and debugging by providing tools for smart reading, searching, and managing code context.
Enables text-only models to understand images through a conversational MCP server, supporting multi-turn follow-ups, URL inputs, and OpenAI-compatible vision APIs.
A local MCP server that ingests entire repositories into a large context window (GLM-5.2 1M tokens) for coding agents, bypassing file limits and reducing query costs.
MCP server for local Ollama vision analysis, enabling text-only agents like Claude Code to inspect images via a single tool. Processes images locally with Ollama, keeping image bytes on the machine and returning text reports.
