iflow-mcp_groundlight-mcp-vision

<p align="center"> <img src="images/image0_and_claude_zoomed_in.png"> </p> <p align="center"> <a href="https://opensource.org/licenses/MIT"> <img src="https://img.shields.io/badge/License-MIT-yellow?style=for-the-badge" alt="License: MIT"> <a href="https://www.groundlight.ai/blog/vision-as-mcp-service"> <img src="https://img.shields.io/badge/Read%20More-Blog-orange?style=for-the-badge" alt="Read More"> </a> </p> </a> </p> # mcp-vision by <img src="images/gl_logo.png" height=25> A Model Context Protocol (MCP) server exposing HuggingFace computer vision models such as zero-shot object detection as tools, enhancing the vision capabilities of large language or vision-language models. This repo is in active development. See below for details of currently available tools. ## Installation Clone the repo: ```bash git clone git@github.com:groundlight/mcp-vision.git ``` Build a local docker image: ```bash cd mcp-vision make build-docker ``` ## Configuring Claude Desktop Add this to your `claude_desktop_config.json`: If your local environment has access to a NVIDIA GPU: ```json "mcpServers": { "mcp-vision": { "command": "docker", "args": ["run", "-i", "--rm", "--runtime=nvidia", "--gpus", "all", "mcp-vision"], "env": {} } } ``` Or, CPU only: ```json "mcpServers": { "mcp-vision": { "command": "docker", "args": ["run", "-i", "--rm", "mcp-vision"], "env": {} } } ``` When running on CPU, the default large-size object detection model make take a long time to laod and run inference. Consider using a smaller model as `DEFAULT_OBJDET_MODEL` (you can tell Claude directly to use a specific model too). **(Beta)** It is possible to run the public docker image directly without building locally, however the download time may interfere with Claude's loading of the server. ```json "mcpServers": { "mcp-vision": { "command": "docker", "args": ["run", "-i", "--rm", "--runtime=nvidia", "--gpus", "all", "groundlight/mcp-vision:late…