groundlight/mcp-vision

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:
Build a local docker image:
Configuring Claude Desktop
Add this to your claude_desktop_config.json:
If your local environment has access to a NVIDIA GPU:
Or, CPU only:
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.
Tools
The following tools are currently available through the mcp-vision server:
1. **locate_objects**
- Description: Detect and locate objects in an image using one of the zero-shot object detection pipelines available
through HuggingFace (list for reference [https://huggingface.co/models?pipeline_tag=zero-shot-object-detection&sort=trending]).
- Input: image_path (string) URL or file path, candidate_labels (list of strings) list of possible objects to detect, hf_model (optional string), will use "google/owlvit-large-patch14" by default, which could be slow on a non-GPU machine
- Returns: List of dicts in HF object-detection format
2. **zoom_to_object**
- Description: Zoom into an object in the image, allowing you to analyze it more closely. Crop image to the object bounding box and return the cropped image. If many objects are present in the image, will return the 'best' one as represented by object score.
- Input: image_path (string) URL or file path, label (string) object label to find and zoom and crop to, hf_model (optional), will use "google/owlvit-large-patch14" by default, which could be slow on a non-GPU machine
- Returns: MCPImage or None
Example in blog post and video
Run Claude Desktop with Claude Sonnet 3.7 and mcp-vision configured as an MCP server in claude_desktop_config.json.
The prompt used in the example video and blog post was:
The image is the first image in the V*Bench/GPT4V-hard dataset and can be found here: https://huggingface.co/datasets/craigwu/vstar_bench/blob/main/GPT4V-hard/0.JPG (use the download link).
Note:
- If you upload the image directly into the conversation with Claude instead of providing a download link, it will not be able to call the tools and will attempt to answer directly.
- On accounts that have web search enabled, Claude will prefer to use web search over local MCP tools AFAIK. Disable web search for best results.