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MCP Vision Adapter

@MetehanYasar11

Repository

Basic information

Category

Developer Tools

License

MIT license

Transports

stdio

Author

MetehanYasar11

About MCP Vision Adapter

No overview available yet

MCP Vision Adapter config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mcp-vision-adapter": {
      "command": "python",
      "args": [
        "-m",
        "mcp_vision_adapter.main"
      ]
    }
  }
}

MCP Vision Adapter tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is MCP Vision Adapter?

MCP Vision Adapter is a universal, open-source MCP (Model Context Protocol) adapter for all CLI projects. It modernizes and connects classic automations, scripts, and detection tools to LLM/agent ecosystems.

How to use MCP Vision Adapter?

Run the YOLOv8 service and the adapter using Docker Compose, or set up a virtual environment, install dependencies, and start both services with Uvicorn. Invoke via HTTP/STDIO or VS Code Copilot Agent mode using endpoints like /execute with tool detect_objects and input parameters.

Key features of MCP Vision Adapter

  • Ultralytics YOLOv8 integration for object detection, segmentation, and pose estimation.
  • REST & STDIO API for flexible integration.
  • Web UI for model management via Streamlit.
  • VS Code Copilot Agent mode support.
  • Easy Docker Compose setup.
  • Video and image analysis capabilities.

Use cases of MCP Vision Adapter

  • Turn legacy CLI automations into LLM/agent tools without rewriting code.
  • Perform object detection on images and videos directly from agent workflows.
  • Hot‑swap detection models on a running service.
  • Connect any CLI‑based tool as an agent tool via the MCP protocol.

Frequently asked questions

1

What is the MCP protocol version used?

MCP Protocol Version `2024-03-26`.

2

How can I run the project without Docker?

Create a Python virtual environment, install dependencies from `yolov8_service/requirements.txt`, then start the YOLOv8 service on port 8080 and the adapter on port 3000.

3

Does MCP Vision Adapter support VS Code Copilot via STDIO?

VS Code Copilot tool execution via STDIO is currently not supported. Use the HTTP API for full integration; this will be fixed in an upcoming update.

4

What if port 3000 is already in use?

Stop the previous process (CTRL‑C) or run the adapter with `--port 3001` and update `mcp.json` accordingly.

5

Where are the custom models stored and how to mount them?

Default weights file is `/weights/yolov8n.pt`. Mount your model folder with `docker run -v ${PWD}/models:/weights`.

Comments

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