vision-opencode-mcp
Provides a tool for reading local images and querying an OpenAI-compatible vision model API to describe them or answer questions about them.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@vision-opencode-mcp What's in the image I just pasted?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
vision-opencode-mcp
Give opencode (or any MCP client) visual reference by calling an OpenAI-compatible vision model API. Exposes one tool:
vision_describe(image_path?, question?)— read a local image and return what the vision model sees. Whenimage_pathis omitted, it automatically picks the newest image (byLastWriteTime) from the pasted-images dir, which is where opencode stores clipboard-pasted images.
Requires only python>=3.10 and the mcp SDK (installed automatically).
Requirements
Python 3.10+
A
VISION_API_KEYfor an OpenAI-compatible vision endpoint (base URL and model are baked in as defaults; override if needed)
Related MCP server: vision-mcp
Install
# from this repo
python -m venv .venv
# Windows:
.venv\Scripts\pip install -e .
# macOS/Linux:
# .venv/bin/pip install -e .
# or with uv (faster)
uv syncThis installs the vision-mcp console script and all dependencies
(mcp, which pulls in mcp-types).
Configure for opencode
Add an mcp entry to your opencode config
(opencode.json or ~/.config/opencode/opencode.json):
{
"mcp": {
"vision": {
"type": "local",
"command": [
"C:\\path\\to\\vision-opencode-mcp\\.venv\\Scripts\\python.exe",
"C:\\path\\to\\vision-opencode-mcp\\server.py"
],
"enabled": true,
"environment": {
"VISION_API_KEY": "your-api-key-here"
}
}
}
}If you installed with uv sync, you can also point at the vision-mcp
console script instead of the venv python:
{
"mcp": {
"vision": {
"type": "local",
"command": ["C:\\path\\to\\vision-opencode-mcp\\.venv\\Scripts\\vision-mcp.exe"],
"enabled": true,
"environment": {
"VISION_API_KEY": "your-api-key-here"
}
}
}
}Restart opencode. The vision_describe tool then appears under the vision
MCP server.
Environment variables
Variable | Default | Required | Description |
| (none) | yes | API key for the vision endpoint. Never hardcoded. |
|
| no | Base URL of the OpenAI-compatible API. |
|
| no | Vision model name. |
| Chrome UA | no | Sent as |
|
| no | HTTP timeout in ms. |
|
| no |
|
|
| no | Directory scanned for the newest image when |
Usage
With image_path:
call vision_describe image_path="D:\pics\shot.png" question="这个截图里报了什么错?"Without image_path (auto-picks the newest pasted image):
call vision_describe question="这张图片里有什么?"CLI (debug)
The repo also ships vision.py, a small command-line version of the same call:
python vision.py <image_path> [question]Prints the model text to stdout; exits 0 on success, 1 on error
(prints ERROR: ...). It reads the same environment variables as the server.
Notes
The API key is read from the environment only — it never appears in the code, so this repo is safe to share.
For clipboard-pasted images in opencode desktop, the tool auto-picks the newest file under the pasted-images dir, so you can just say "看这张图" and the tool picks it up.
Available Tools
1 toolvision_describeA
Give the model visual reference: read a local image file and return what a vision-capable model sees/understands about it. Use whenever you need to see or interpret an image (screenshots, diagrams, photos).
Inputs: question, image_path.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
Scored across 1 tool
With only one tool, there is no possibility of confusion or overlap. The tool's purpose is clearly described and distinct by virtue of being the only option.
The single tool name 'vision_describe' follows a consistent verb_noun pattern. With only one tool, internal naming consistency is trivially maintained.
One tool is borderline thin for a server whose name suggests broader vision capabilities. However, the narrow focus on image description makes the count acceptable, if minimal.
The tool covers the core need of interpreting local image files, but lacks support for remote images or additional vision operations (e.g., OCR, image metadata). These are minor gaps for the stated purpose.
Maintenance
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