opencode-vision 1.0.0

Model-agnostic image analysis via MCP for OpenCode text-only models. Vision proxy server using Google Gemini + tesseract.

opencode-vision 👁️

Model-agnostic image analysis via MCP for OpenCode.

Give vision capabilities to any text-only model — big-pickle, DeepSeek, MiMo, MiniMax, or any other model that can't process images natively.

pip install opencode-vision

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The Problem

OpenCode supports many models, but most open-weight and free models are text-only. When you paste an image or try to read() one, you get:

ERROR: Cannot read image (this model does not support image input).

This is not a configuration issue — it's a fundamental limitation of the model architecture. Text-only models have no visual neurons.

The Solution

opencode-vision is an MCP server that acts as a "guide dog" for text-only models. It runs as an independent process and handles image analysis via:

1. Google Gemini Vision API (FREE tier, 1,500 requests/day)
2. Local tesseract OCR (fast, private, works offline)

It returns plain text descriptions that any model can understand — no vision capabilities needed on the model's side.

                    ┌─────────────────────┐
                    │  opencode-vision     │
  [big-pickle] ────►│  MCP Server          │────► Google Gemini API
  [DeepSeek]   ────►│  (Python process)    │────► tesseract OCR (local)
  [MiMo]       ────►│  Returns TEXT only   │
                    └─────────────────────┘

Quick Start

1. Install

pip install opencode-vision

2. Get a Gemini API key

Get a free key at aistudio.google.com (1,500 requests/day, no credit card required).

Set it in ~/.config/opencode/.env:

echo 'GOOGLE_API_KEY=your_key_here' >> ~/.config/opencode/.env

Or export it directly:

export GOOGLE_API_KEY=your_key_here

3. Add to OpenCode config

Add this to ~/.config/opencode/opencode.json:

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "vision": {
      "type": "local",
      "command": ["python3", "-m", "opencode_vision.server"],
      "enabled": true,
      "timeout": 30000
    }
  }
}

4. Restart OpenCode

Start a new session. The vision_describe, vision_ocr, and vision_analyze tools will be available to all models — even text-only ones.

5. Ask about images

User: What's in this image?
Model: [calls vision_describe("/path/to/image.png")]
       "A dark gradient banner with 'Nicolás Ríos Herrera'..."

Tools

Tool	Description	When to use
vision_describe(path, prompt?)	Describe an image in detail	"What does this show?"
vision_ocr(path)	Extract all visible text	"What text is in this screenshot?"
vision_analyze(path)	Metadata + description + OCR	Comprehensive understanding

Requirements

Component	Required?	Notes
Python >= 3.10	✅ Required
GOOGLE_API_KEY	✅ Required	Get free at aistudio.google.com
pillow	📦 Recommended	pip install pillow for metadata + auto-resize
tesseract-ocr	🔧 Recommended	For local OCR. apt install tesseract-ocr or brew install tesseract

The server auto-detects the API key from (in order):

1. GOOGLE_API_KEY environment variable
2. GOOGLE_GENERATIVE_AI_API_KEY environment variable
3. ~/.config/opencode/.env file
4. ~/.env file
5. $PWD/.env file

CLI Usage (without OpenCode)

# Start MCP server (for OpenCode integration)
opencode-vision

# Direct analysis
opencode-vision describe ~/screenshot.png
opencode-vision ocr ~/scanned-document.png
opencode-vision analyze ~/photo.jpg

# Custom prompt
opencode-vision describe ~/chart.png "What are the values in this chart?"

Architecture

Why Python?

All existing MCP vision servers for OpenCode are Node.js/TypeScript and require npm install or npx. opencode-vision is pure Python because:

• Python is already installed on every developer machine
• pillow (PIL) is the standard image processing library
• tesseract has first-class Python bindings
• Zero node_modules, zero npm, zero npx
• The MCP protocol is simple JSON-RPC over stdio — no framework needed

Hybrid OCR Strategy

                    ┌──────────────┐
  User image ──────►│ tesseract    │───► Text found? ──► Return
                    │ (local,      │
                    │  private,    │
                    │  offline)    │
                    └──────┬───────┘
                           │ No text / failed
                           ▼
                    ┌──────────────┐
                    │ Gemini       │───► Return
                    │ Vision API   │
                    │ (FREE tier)  │
                    └──────────────┘

Cost: $0

• Gemini 2.5 Flash: 1,500 free requests/day via Google AI Studio API key
• tesseract: free and local for OCR
• Pillow: free and local for metadata
• No OpenCode Go credits consumed — the API call happens in the vision server, not through OpenCode's model proxy

Comparison with Alternatives

Feature	opencode-vision	opencode-minimax-easy-vision	qwen-vision-mcp	opencode-image-proxy
Runtime	Python (stdlib)	Node.js + npm	Node.js + npm	Node.js
Dependencies	pip install opencode-vision	npm install + MiniMax API key	npx + Ollama (6GB RAM)	npx
OCR	Local tesseract + Gemini fallback	None (API only)	None (API only)	None (API only)
API cost	$0 (Gemini FREE tier)	MiniMax pricing	$0 (local) or Ollama Cloud	OpenCode credits
Auto .env	✓ Reads ~/.config/opencode/.env	✗ Manual env vars	✗ Manual env vars	✗ Manual config
Image resize	✓ Pillow auto-resize	✗	✗	✗
Install size	~200 KB (pure Python)	~30 MB (node_modules)	~30 MB + ~6GB (Ollama model)	~30 MB

Why "Model-Agnostic"?

The key architectural insight: the model never needs to see pixels. The MCP server does all the visual processing externally and returns text. This means:

• Works with any text-only model (big-pickle, DeepSeek, MiMo, MiniMax, GLM, etc.)
• Works with any multimodal model too (it doesn't interfere)
• No model-specific configuration
• No provider-specific setup
• The model can be changed at any time without reconfigureing vision

License

MIT

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Built with ❤️ by Nicolás Ríos Herrera for the OpenCode community.