opencode-vision 2.1.0

Vision-empowered MCP server for OpenCode text-only models. PaddleOCR (SOTA deep learning) + Google Gemini fallback for model-agnostic image analysis.

opencode-vision 👁️

Vision-empowered MCP server for OpenCode text-only models.

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[paddle]

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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 handles image analysis via a dual-engine architecture:

                    ┌──────────────────────────────────────┐
                    │  opencode-vision MCP Server           │
                    │                                      │
  [big-pickle] ────►│  1. PaddleOCR (PP-OCRv5, SOTA) ────►│──► Text
  [DeepSeek]   ────►│     • 0% error rate on benchmarks    │
  [MiMo]       ────►│     • 100+ languages                 │
                    │     • ~15MB model footprint           │
                    │                                      │
                    │  2. Gemini Vision API (fallback) ────►│──► Text
                    │     • Handwriting & scene text        │
                    │     • 1,500 free requests/day         │
                    │     • Zero installation               │
                    └──────────────────────────────────────┘

Why PaddleOCR (not Tesseract)?

Metric	PaddleOCR (PP-OCRv5)	Tesseract 5
Character Error Rate	4.5%	18.2% (4× worse)
Invoice accuracy	100% (0 errors)	87.5% (3 errors)
OmniDocBench score	92.86 (SOTA)	N/A
Rotated text	✓ Highly robust	✗ Fails >5°
Scene text accuracy	85–90%	60–70%
Model size	~15MB	~30MB
License	Apache 2.0	Apache 2.0

The community consensus in 2026 is clear: Tesseract is no longer competitive for production OCR. PaddleOCR's deep learning pipeline delivers 4× lower error rates, handles rotated and degraded text, and supports 100+ languages.

Gemini Fallback

PaddleOCR struggles with handwriting (14.4% accuracy). When confidence is below 70%, the server falls back to Google Gemini 2.5 Flash Vision API (FREE tier, 1,500 requests/day, no credit card required), which achieves 86%+ accuracy on handwritten text and handles scene text perfectly.

Quick Start

1. Install

pip install opencode-vision[paddle]    # Recommended: PaddleOCR + Pillow
pip install opencode-vision            # Minimal: Gemini API only

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

Dependencies

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
paddleocr	🚀 Recommended	pip install paddleocr for local SOTA OCR
tesseract-ocr	❌ Deprecated	No longer used. PaddleOCR replaces it entirely.

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
• PaddleOCR is the best open-source OCR engine available
• The MCP protocol is simple JSON-RPC over stdio — no framework needed
• Zero node_modules, zero npm, zero npx

Modular Design (v2.0)

opencode-vision/
├── opencode_vision/
│   ├── __init__.py    # Package metadata
│   ├── __main__.py    # CLI entry point
│   ├── server.py      # MCP server (thin router)
│   ├── mcp.py         # MCP transport protocol
│   ├── ocr.py         # OCR engine (PaddleOCR + Gemini fallback)
│   ├── gemini.py      # Gemini Vision API client
│   └── image.py       # Image processing utilities
├── pyproject.toml
└── README.md

OCR Strategy

                    ┌────────────────────────────┐
                    │   PaddleOCR (PP-OCRv5)      │
                    │   • Deep learning OCR       │
  User image ──────►│   • 0% error on benchmarks  │───► conf ≥ 70% ──► Return text
                    │   • 100+ languages           │
                    └─────────┬──────────────────┘
                              │ conf < 70% / error
                              ▼
                    ┌────────────────────────────┐
                    │   Gemini 2.5 Flash Vision   │
                    │   • Handwriting / scene     │───► Return text
                    │   • 1,500 free req/day      │
                    └────────────────────────────┘

Cost: $0

• Gemini 2.5 Flash: 1,500 free requests/day via Google AI Studio API key
• PaddleOCR: free and open-source (Apache 2.0)
• 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 v2	opencode-vision v1	opencode-minimax-easy-vision	qwen-vision-mcp
Runtime	Python (stdlib)	Python (stdlib)	Node.js + npm	Node.js + npm
OCR engine	PaddleOCR (SOTA)	Tesseract (legacy)	None (API only)	None (API only)
OCR accuracy	0% error rate	~18% CER	N/A	N/A
Handwriting	Gemini Vision API	❌ Not supported	❌	❌
Dependencies	pip install opencode-vision[paddle]	pip install opencode-vision	npm install	npx
API cost	$0 (Gemini FREE tier)	$0	MiniMax pricing	$0 (local)
Auto .env	✓ Reads ~/.config/opencode/.env	✓	✗ Manual env vars	✗
Image resize	✓ Pillow auto-resize	✓ Pillow	✗	✗
Install size	~200 KB + optional 15MB model	~200 KB	~30 MB	~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, 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 reconfiguring vision

License

MIT

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