OpenCode plugin that enables vision support for Minimax models by saving pasted images and injecting MCP tool instructions
npm install opencode-minimax-easy-visionMiniMax Easy Vision is a plugin for OpenCode that enables vision support for models that lack native image attachment support.
Originally built for MiniMax models, it can be configured to work with any model that requires MCP-based image handling.
It restores the "paste and ask" workflow by automatically saving image assets and routing them through the MiniMax Coding Plan MCP
See how it works:
https://github.com/user-attachments/assets/df396c6c-6fa8-46b8-8984-c003ecf1c12c
https://github.com/user-attachments/assets/826f90ea-913f-427e-ace8-0b711302c497
When using MiniMax models (like MiniMax M2.1) in OpenCode, native image attachments aren't supported.
These models expect the MiniMax Coding Plan MCP's understand_image tool, which requires an explicit file path. This breaks the normal flow:
* Ignored images: Pasted images are simply ignored by the model.
* Manual steps: You have to save screenshots manually, find the path, and reference it in your prompt.
* Broken flow: The "paste and ask" experience available with Claude or GPT models is lost.
This plugin automates the vision pipeline so you don't have to think about it.
How it works:
1. Detects when a configured model is active.
2. Intercepts images pasted into the chat.
3. Saves them to a temporary local directory.
4. Injects the necessary context for the model to invoke the understand_image tool with the correct path.
Result: You just paste the image and ask your question just like how you do with Claude or GPT models. The plugin handles the rest.
By default, the plugin activates for MiniMax models:
* Provider ID containing minimax
* Model ID containing minimax or abab
Examples:
* minimax/minimax-m2.1
* minimax/abab6.5s-chat
You can enable this for other models by creating a config file.
#### Locations (Priority Order)
1. Project level: .opencode/opencode-minimax-easy-vision.json
2. User level: ~/.config/opencode/opencode-minimax-easy-vision.json
#### Config Format
``json`
{
"models": ["minimax/", "opencode/", "*/glm-4.7-free"]
}
#### Pattern Syntax
| Pattern | Matches |
| ---------------- | --------------------------------------- |
| * | Match ALL models |minimax/*
| | All models from the minimax provider |*/glm-4.7-free
| | Specific model from any provider |opencode/*
| | All models from the opencode provider |/abab
| | Any model containing abab |
#### Wildcard Rules
suffix matches values ending with suffix
matches values starting with prefix
matches everythingtext*
matches values containing text
If the config is missing or empty, it defaults to MiniMax-only behavior.
#### Configuration Examples
Enable for all models:
`json`
{
"models": ["*"]
}
Specific providers:
`json`
{
"models": ["minimax/", "opencode/", "google/*"]
}
Mix of providers and models:
`json`
{
"models": ["minimax/", "opencode/gpt-5-nano", "/claude-3-7-sonnet*"]
}
By default, the plugin uses the mcp_minimax_understand_image tool from the MiniMax Coding Plan MCP. You can configure a different tool for image analysis:
`json`
{
"models": ["*"],
"imageAnalysisTool": "mcp_openrouter_analyze_image"
}
> Note: The imageAnalysisTool value is the tool name, not the MCP server name. MCP servers expose one or more tools—for example, the MiniMax Coding Plan MCP server exposes the mcp_minimax_understand_image tool. When configuring a custom tool, use the exact tool name as it appears in OpenCode (typically prefixed with mcp_).
This allows you to use tools from other MCP servers that provide image analysis capabilities, such as:
* openrouter-image-mcp - Uses OpenRouter with GPT-4V, Claude, Gemini
* mcp-image-recognition - Uses Anthropic/OpenAI Vision APIs
* Peekaboo - macOS screenshot + AI analysis
The plugin will instruct the model to use the configured tool. The tool should accept an image file path as input.
* PNG
* JPEG
* WebP
(Limited by the MiniMax Coding Plan MCP understand_image tool.)
Just add the plugin to the plugin array in your opencode.json file:
`json`
{
"$schema": "https://opencode.ai/config.json",
"plugin": ["opencode-minimax-easy-vision"]
}
1. Clone the repository.
2. Build the plugin:
`bash`
npm install && npm run build
3. Copy the built into your OpenCode plugin directory.
The MiniMax Coding Plan MCP server must be configured in your opencode.json:
`json`
{
"mcp": {
"MiniMax": {
"command": "uvx",
"args": ["minimax-coding-plan-mcp"],
"env": {
"MINIMAX_API_KEY": "your-api-key-here",
"MINIMAX_API_HOST": "https://api.minimax.io"
}
}
}
}
1. Select a supported model in OpenCode.
2. Paste an image (Cmd+V / Ctrl+V).
3. Ask a question about it, just like how you do for other models with native vision support.
> You: [pasted screenshot] Why is this failing?
>
> Model: I'll check the image using the understand_image tool.[Calls mcp_minimax_understand_image path="/tmp/xyz.png"]
>
>
> Model: The error suggests a syntax error on line 12.
`bash`
npm install
npm run build
The built plugin will be available at dist/index.js`
GPL-3.0. See LICENSE.md
* OpenCode Official Website
* OpenCode Plugins Documentation
* MiniMax Official Website
* MiniMax Coding Plan MCP Repository
* MiniMax API Documentation