# Blind Vision Mcp

> MCP server that gives vision to any text-only LLM — 100% local, no API bills. Gemma 4 E2B (LiteRT) for vision + SDXL-Turbo for image gen

- **Type:** MCP server
- **Install:** `agentstack add mcp-alexjm19-blind-vision-mcp`
- **Verified:** Pending review
- **Seller:** [alexjm19](https://agentstack.voostack.com/s/alexjm19)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [alexjm19](https://github.com/alexjm19)
- **Source:** https://github.com/alexjm19/blind-vision-mcp

## Install

```sh
agentstack add mcp-alexjm19-blind-vision-mcp
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# blind-vision-mcp

> Give vision to any text-only LLM — 100% local, no API costs, your privacy intact.

[]()
[]()
[]()
[]()
[]()
[]()
[]()

---

## Why this exists

**I use DeepSeek v4 Flash** — an incredible text model. But it's **blind**. It can't see screenshots, images, or UI layouts.

I was tired of:
- Paying **$20-200/month** for vision-capable APIs (GPT-4 Vision, Claude)
- Sending **sensitive screenshots to the cloud**
- **Context switching** between coding and describing images manually

So I built **blind-vision-mcp**: an MCP server that sits between your text LLM and your desktop, letting it "see" through an **on-device vision model** — Google's **Gemma 4 E2B** running via **LiteRT**.

### My specific use case

I control an **Android emulator** that takes **screenshots** of the device. DeepSeek v4 Flash reads those screenshots via blind-vision-mcp and tells the emulator what to do next. It works like this:

```
Emulator takes screenshot → blind-vision-mcp analyzes it with Gemma 4 → 
DeepSeek reads the description → decides next action → ADB command
```

All of this happens **locally**, **privately**, and **without paying per-token API fees**.

---

## What it does

| Capability | Status | Model |
|-----------|--------|-------|
| 👁️ **Image analysis** | ✅ **Stable** | Gemma 4 E2B via LiteRT (~2.6 GB VRAM) |
| 🔄 **Image comparison** | ✅ **Stable** | Gemma 4 E2B via LiteRT |
| 🎨 **Image generation** | ✅ **Stable** | SDXL-Turbo (fp16, ~7 GB VRAM, no HF token needed) |
| ✏️ **Image editing** | 🧪 **In development** | Coming soon |

---

## Key features

- **No API keys needed** for vision — runs 100% on your GPU
- **~2.6 GB VRAM** for vision (not 10+ GB like other solutions)
- **GPU-first** — falls back to CPU if GPU fails
- **Google LiteRT** — same stack powering Gemini Nano on Android
- **Macro-friendly** — perfect for automating emulators, browsers, UIs
- **Works with any MCP client** — OpenCode, Claude Desktop, Cursor, Cline

---

## Quick Start

```bash
# 1. Prerequisites
curl -LsSf https://astral.sh/uv/install.sh | sh

# 2. Clone and install
git clone https://github.com/alexjm19/blind-vision-mcp.git
cd blind-vision-mcp
uv sync

# 3. Import the vision model (one-time, downloads ~2.6 GB)
litert-lm import \
  --from-huggingface-repo litert-community/gemma-4-E2B-it-litert-lm \
  gemma-4-E2B-it.litertlm \
  gemma4-vision

# 4. Start the server
uv run blind-vision-mcp
```

> **For image generation/editing**: Create a `.env` file with your HF token:
> ```
> HF_TOKEN=hf_your_token_here
> ```
> Then accept terms at https://huggingface.co/black-forest-labs/FLUX.1-schnell

---

## Configuration for OpenCode

Add to your `opencode.json`:

```json
{
  "mcpServers": {
    "blind-vision-mcp": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/path/to/blind-vision-mcp",
        "blind-vision-mcp"
      ]
    }
  }
}
```

---

## Self-Configuration Prompt

Give this prompt to any LLM to have it configure itself to use blind-vision-mcp:

```markdown
To give yourself vision capabilities, I have installed **blind-vision-mcp** on this machine.
It is an MCP server that allows text-only models like you to analyze images and generate images.

## What you need to do

1. Check if blind-vision-mcp is installed:
   ```bash
   pip show blind-vision-mcp
   ```

2. If not installed, ask the user to run:
   ```bash
   pip install blind-vision-mcp
   ```

3. Import the vision model (one-time):
   ```bash
   litert-lm import \
     --from-huggingface-repo litert-community/gemma-4-E2B-it-litert-lm \
     gemma-4-E2B-it.litertlm \
     gemma4-vision
   ```

4. When you need to see an image, call this tool:
   - `vision_describe(image="/path/to/image.jpg")` for detailed analysis
   - `vision_compare(image_a="...", image_b="...")` to compare

5. When you need to generate an image:
   - `image_generate(description="...")`

The server starts automatically on first use. The vision model uses ~2.6 GB VRAM via LiteRT.
```

---

## Usage Examples

```bash
# Analyze a screenshot (perfect for emulator control)
vision_describe(image="/path/to/screenshot.png")

# Compare before/after
vision_compare(image_a="/path/to/before.png", image_b="/path/to/after.png")

# Generate an image (beta)
image_generate(description="a beautiful landscape")

# Check server status
get_status()
```

---

## How vision works (the cool part)

```
┌─────────────────────────────────────────────────────────┐
│  DeepSeek v4 Flash (text-only)                          │
│  "What's on the screen? → vision_describe(screenshot)"  │
└────────────────────────┬────────────────────────────────┘
                         │ MCP protocol (stdin/stdout)
┌────────────────────────▼────────────────────────────────┐
│  blind-vision-mcp server                                 │
│  ┌────────────┐  ┌──────────────┐  ┌──────────────────┐  │
│  │ tools.py    │→│ LiteRT server │→│ Gemma 4 E2B     │  │
│  │ (MCP tools) │  │ (port 9380)  │  │ (2.6 GB VRAM)   │  │
│  └────────────┘  └──────────────┘  └──────────────────┘  │
└─────────────────────────────────────────────────────────┘
```

The vision model (**Gemma 4 E2B**) runs entirely on your GPU via **Google's LiteRT runtime**. No data ever leaves your machine. The model is pre-quantized (mixed 2/4/8-bit) and loads directly at ~2.6 GB — no "load BF16 first then quantize" memory spike.

---

## Why not just use a vision LLM?

| Solution | Cost | Privacy | VRAM | Quality |
|----------|------|---------|------|---------|
| GPT-4 Vision | $10-20/mo | ❌ Cloud | N/A | Excellent |
| Claude Vision | $20/mo | ❌ Cloud | N/A | Excellent |
| Qwen2-VL-7B (local) | Free | ✅ Local | ~10 GB VRAM | Good |
| **blind-vision-mcp** | **Free** | **✅ Local** | **~2.6 GB VRAM** | **Great** |

---

## Requirements

| Component | Minimum |
|-----------|---------|
| **GPU** | NVIDIA ≥8 GB VRAM |
| **RAM** | 16 GB |
| **Storage** | 5 GB free for vision model + 7 GB for gen model |
| **CUDA** | 12.x |

---

## Project Status

- **Vision**: ✅ Stable and tested
- **Image generation**: ✅ Stable (SDXL-Turbo, pure GPU, no offload)
- **Image editing**: 🧪 In development
- **Version**: 0.2.0 — API may change

---

## License

MIT — see [LICENSE](LICENSE).

---

## Support

[](https://buymeacoffee.com/alexjmwarea)

## Star History

 
   
   
   
 

If this saves you from another API bill, **⭐ star the repo**. It helps others find local-first AI tools.

---

Built with ❤️ by [alexjm19](https://github.com/alexjm19)

## Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [alexjm19](https://github.com/alexjm19)
- **Source:** [alexjm19/blind-vision-mcp](https://github.com/alexjm19/blind-vision-mcp)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** yes
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** yes
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: flagged — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/mcp-alexjm19-blind-vision-mcp
- Seller: https://agentstack.voostack.com/s/alexjm19
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.