@winton979/vision-mcp

MCP server that exposes an analyze_image tool backed by an OpenAI-compatible vision LLM (GPT-4o, Qwen-VL, etc.).

What it does

Provides a single MCP tool analyze_image that accepts an image via:

• path — local file path

• url — public http(s) URL

• base64 — raw base64 string (with or without data: prefix)

and returns a text description from the configured vision model.

Related MCP server: VisionPower

Prerequisites

• Node.js ≥ 18 (global fetch required)

Configuration

Set these environment variables when configuring the MCP server:

Claude Code setup

macOS / Linux

Add to ~/.claude.json or ~/.claude/.mcp.json:

{
  "mcpServers": {
    "vision": {
      "command": "npx",
      "args": ["-y", "@winton979/vision-mcp"],
      "env": {
        "VISION_BASE_URL": "<your-base-url>",
        "VISION_API_KEY": "<your-api-key>",
        "VISION_MODEL": "<your-model>"
      }
    }
  }
}

Windows

{
  "mcpServers": {
    "vision": {
      "command": "cmd",
      "args": ["/c", "npx", "-y", "@winton979/vision-mcp"],
      "env": {
        "VISION_BASE_URL": "<your-base-url>",
        "VISION_API_KEY": "<your-api-key>",
        "VISION_MODEL": "<your-model>"
      }
    }
  }
}

Codex setup

macOS / Linux

Add to ~/.codex/config.toml:

[mcp_servers.vision-mcp]
type = "stdio"
command = "npx"
args = ["-y", "@winton979/vision-mcp"]
env = { VISION_BASE_URL = "<your-base-url>", VISION_API_KEY = "<your-api-key>", VISION_MODEL = "<your-model>" }

Windows

[mcp_servers.vision-mcp]
type = "stdio"
command = "npx"
args = ["-y", "@winton979/vision-mcp"]
env = { VISION_BASE_URL = "<your-base-url>", VISION_API_KEY = "<your-api-key>", VISION_MODEL = "<your-model>" }

Tool: analyze_image

task

Selects an optimized built-in system context + default instruction, so callers don't have to hand-write a prompt for common cases:

If prompt is also provided, it takes precedence as the specific question while the task's specialized context still applies — e.g. task=ocr, prompt="what is the total amount?".

response_mode

• markdown (default) — structured report (## Summary / ## Visible Objects / ## Text / ## Findings / ## Uncertainties)

• json — a single JSON object (summary, objects, text, findings, uncertainties) for easy parsing; the tool returns only the JSON, with no extra metadata appended

• plain_text — unstructured text

json mode sends response_format: { type: "json_object" }. Some OpenAI-compatible gateways do not support this field and may return HTTP 400; in that case fall back to markdown.

Built-in behavior

A fixed base system prompt is always applied — it enforces observable-facts-only reporting, exact text preservation, and prompt-injection protection (text inside the image is treated as content, never as instructions). The optional system parameter is appended after these rules and cannot override them.

Tips: avoid [image] tag conversion (Windows)

When you paste a local image path into Claude Code, the CLI may auto-convert it into an [image] tag and inline the bytes, which fails on models or not stable that do not accept image input. To keep the raw path intact, use ImageClipboardModify — it appends a fixed prefix to clipboard image paths so they are no longer recognized as images, letting analyze_image receive the path verbatim. for use mcp vision always

中文说明:Claude Code 会把粘贴的本地图片路径自动识别为 [image] 标签,导致不支持图片的模型偶尔不稳定或者报错。可使用 ImageClipboardModify 给剪切板图片附加一段固定文字,绕过识别,让路径以纯文本形式传入 analyze_image，使用使用mcp vision解析

Local development

git clone https://github.com/winton979/vision-mcp.git
cd vision-mcp
npm install
npm run build

# Run smoke test
SMOKE_IMAGE=/path/to/test.png VISION_API_KEY=sk-... npm run smoke

License

MIT