Vision MCP

The Universal Vision-Language Model MCP Server — Image Understanding & OCR for Any MCP Client

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Vision MCP is a universal MCP server that plugs any OpenAI-compatible vision-language model into any MCP client — Claude Code, Reasonix, Cursor, Windsurf, VS Code and more. It exposes two tools: image understanding (describe & Q&A) and OCR (text extraction). Your images never leave your machine.

It is not tied to any model vendor: point it at local Ollama, vLLM, LM Studio, or any remote OpenAI-compatible endpoint — just change configuration, not code.

✨ Highlights

• 🧠 Model-agnostic — Works with any vision model exposing an OpenAI-compatible API (qwen2.5vl, llama3.2-vision, minicpm-v, GPT-4o, Gemini…), plus Ollama's native API for keep_alive residency

• 🔌 Client-agnostic — A single server, usable from any MCP client via stdio

• 📦 Zero-dependency distribution — Ship a single vision-mcp.exe (Node SEA); recipients need no Node.js, no npm, no Python

• 🖼️ Multi-image — Pass multiple images in one call natively

• 🔒 Privacy-first — 100% local inference; images never leave the machine (unless you point it at a remote endpoint)

• 🧭 Smart path handling — Absolute, relative, and ~ paths; clear Chinese/English error messages

• ⚙️ Fully configurable — Endpoint, model name, API format, keep-alive and timeout via environment variables

Related MCP server: readpic MCP Server

🏗️ Architecture

┌──────────────┐   MCP stdio    ┌────────────────────┐   HTTP   ┌───────────────────────┐
│ Any MCP      │ ─────────────→ │  vision-mcp server │ ──────→ │  Vision backend       │
│ client       │ ←───────────── │  (Node/TS or exe)  │ ←────── │  Ollama | vLLM | ...  │
└──────────────┘   text result  └────────────────────┘          └───────────────────────┘

The server is a thin, stateless bridge: it receives image paths from the MCP client, base64-encodes them, forwards them to the vision backend, and returns the model's text answer.

🛠️ Tools

🚀 Quick Start

1. Run a vision backend (e.g. Ollama)

ollama pull qwen2.5vl:3b
ollama serve

Verify: curl http://localhost:11434/api/tags should list your model.

2. Get the server

Option A — Single-file executable (no runtime needed):

Download vision-mcp.exe from the Releases page.

Option B — Run from source:

npm install
npm run build

3. Register in your MCP client

Create/merge .mcp.json in your project root (or ~/.claude/.mcp.json for Claude Code globally):

{
  "mcpServers": {
    "vision-mcp": {
      "command": "/path/to/vision-mcp.exe",
      "args": []
    }
  }
}

Running from source? Use "command": "node", "args": ["/path/to/dist/server.cjs"].

4. Use it

「Use vision-mcp to look at ./screenshot.png and describe it」
「OCR the text in ./doc.jpg」

⚙️ Configuration

All settings are optional environment variables, passed via the env field in .mcp.json:

Switching backends (example: vLLM)

{
  "mcpServers": {
    "vision-mcp": {
      "command": "/path/to/vision-mcp.exe",
      "args": [],
      "env": {
        "VLM_API_MODE": "openai",
        "VLM_BASE_URL": "http://192.168.1.10:8000/v1",
        "VLM_MODEL": "Qwen/Qwen2.5-VL-3B-Instruct"
      }
    }
  }
}

That's it — no code changes, no rebuild when switching providers.

📦 Distribution to Others

Build the single-file executable:

npm run build:exe     # outputs dist/vision-mcp.exe (~89 MB)

Recipients only need to:

1. Have their own vision backend (Ollama + model, or set VLM_BASE_URL to a shared/remote service)

2. Register the exe in any MCP client — no Node, no Python

❓ FAQ

Because the server intentionally exposes exactly two tools: describe_image (image understanding) and ocr_image (OCR). The executable path shown next to the server is the server program itself, not a third tool.

The model is loaded on first use (a few seconds to tens of seconds). Set VLM_KEEP_ALIVE=30m (default) so the model stays resident — subsequent calls return in under a second.

The config is fine; the vision backend is down. Start it: ollama serve, and verify with curl http://localhost:11434/api/tags.

The SEA-built exe embeds your bundle into a copy of node.exe, which invalidates the original Microsoft signature and can trigger false positives. Alternative: build a native binary with Bun: bun build --compile src/server.ts --outfile dist/vision-mcp.exe.

describe_image/ocr_image require a vision (multimodal) model. A text-only model will not read images.

🧑‍💻 Development

npm install          # install dependencies
npm run build        # esbuild bundle → dist/server.cjs
npm test             # end-to-end test against a real backend (official MCP client over stdio)
npm run build:exe    # package single-file executable (Node SEA)

End-to-end tests connect through the official MCP SDK client and exercise both tools, multi-image input, and error paths.

📁 Project Structure

├── src/
│   └── server.ts              # MCP server source (TypeScript)
├── scripts/
│   ├── test-client.mjs        # End-to-end test (official MCP client)
│   └── build-sea.mjs          # Single-file executable builder
├── package.json               # Build/test/package scripts
└── .mcp.json                  # Example registration config

🤝 Contributing

Issues and pull requests are welcome! For new features, please open an issue to discuss before submitting a PR.

• Run npm run build to ensure the TypeScript compiles

• Run npm test to ensure existing behavior is preserved

• Keep changes focused and documented

📄 License

MIT © Ameng