codex-vision-mcp
Provides image understanding capabilities by leveraging OpenAI's vision models through the Codex app-server, allowing analysis of images such as screenshots, diagrams, and charts via a single tool.
Click on "Install 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., "@codex-vision-mcpanalyze this error screenshot and explain the cause"
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.
codex-vision-mcp
Expose Codex app-server image understanding to MCP clients as one tool:
understand_image
The tool accepts one or more local image paths or HTTP(S) image URLs and a free-form question. It is intended for screenshots, OCR, error screenshots, UI review, diagrams, charts, and visual comparison.
Install
git clone https://github.com/lyd123qw2008/codex-vision-mcp.git
cd codex-vision-mcp
npm run smokeTool arguments:
{
"images": ["D:\\tmp\\demo.png"],
"question": "这张图展示了什么?",
"detail": "high"
}Related MCP server: agent-vision-mcp
Authentication
This server does not need a separate vision API key. It starts the local codex app-server and reuses the Codex CLI authentication/configuration on this machine.
On a new machine, authenticate Codex first:
codex loginAdvanced overrides:
CODEX_JS: full path to@openai/codex/bin/codex.jsCODEX_BIN: command or executable to start CodexCODEX_VISION_MODEL: model for Codex app-server, defaultgpt-5.4CODEX_VISION_WORKDIR: base directory for relative image pathsCODEX_VISION_TIMEOUT_MS: request timeout, default180000CODEX_VISION_MAX_IMAGES: max images per call, default5CODEX_VISION_MAX_IMAGE_MB: max local image size, default20
Claude Code configuration
Add this MCP server from the repository directory:
$server = (Resolve-Path .\src\server.js).Path
claude mcp add -s user codex-vision-mcp -- node $serverIf relative image paths should resolve from a specific directory:
$server = (Resolve-Path .\src\server.js).Path
$workspace = "C:\path\to\workspace"
claude mcp add -s user codex-vision-mcp --env CODEX_VISION_WORKDIR=$workspace -- node $serverEquivalent user-level configuration shape:
{
"codex-vision-mcp": {
"type": "stdio",
"command": "node",
"args": ["C:\\path\\to\\codex-vision-mcp\\src\\server.js"],
"env": {
"CODEX_VISION_WORKDIR": "C:\\path\\to\\workspace",
"CODEX_VISION_MODEL": "gpt-5.4"
}
}
}Example prompt:
请调用 understand_image 分析 D:\tmp\error.png,告诉我这个报错原因和下一步怎么排查。Verify Claude Code can see the tool:
claude mcp list
claude --print "请使用 understand_image 工具分析本地图片 D:\tmp\demo.png,问题是:这张图展示了什么?" --allowedTools "mcp__codex-vision-mcp__understand_image"If an already-open Claude Code session does not show the tool, restart that session after changing MCP configuration.
When using a routed text model such as GLM through Claude Code, the MCP tool result may appear first in the terminal under the tool output line. Claude Code still needs one more model turn to convert that tool result into the final assistant reply, so the final reply can lag behind the tool result.
Local smoke test
npm run smokeThis only validates MCP initialize and tools/list. Real image understanding is exercised from an MCP client such as Claude Code.
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