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image-mcp

by OoadaioO
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image-mcp

基于 DashScope 视觉模型的本地 MCP server，用于识别本地或远程图片，并默认以 XML 形式返回图片内容，帮助 AI 理解图片。

安装

npm install

Related MCP server: image-mcp

MCP 配置

模型配置通过 MCP client 的 env 注入到 server 进程，不需要也不应该作为 tool 参数传递。

{
  "mcpServers": {
    "image-mcp": {
      "command": "node",
      "args": ["/absolute/path/to/image-mcp/src/index.mjs"],
      "env": {
        "DASHSCOPE_API_KEY": "your-api-key",
        "VISION_MODEL": "qwen-vl-plus",
        "DASHSCOPE_BASE_URL": "https://dashscope.aliyuncs.com/compatible-mode/v1"
      }
    }
  }
}

DASHSCOPE_BASE_URL 可省略，默认使用 https://dashscope.aliyuncs.com/compatible-mode/v1。

Tool

recognize_image

  • image_path: 本地图片路径，和 image_url 二选一。

  • image_url: 远程图片 URL，和 image_path 二选一。

  • prompt: 可选识图提示词，默认值为 识别图像内容，使用xml形式输出，帮助ai理解图片内容。

  • max_tokens: 可选最大输出 token 数，默认 1024。

CLI

npm run vision -- ./dist/ScreenShot_2026-08-01_155219_072.png "识别图像内容，使用xml形式输出，帮助ai理解图片内容"
npm run vision -- --url https://example.com/image.png

本地 npm 使用

如果只是本机使用，不需要发布到 npm registry。推荐用 npm link 或 npm pack。

方式一：npm link

在项目目录执行：

npm install
npm link

然后 MCP 配置可以直接使用全局命令：

{
  "mcpServers": {
    "image-mcp": {
      "command": "image-mcp",
      "args": [],
      "env": {
        "DASHSCOPE_API_KEY": "your-api-key",
        "VISION_MODEL": "qwen-vl-plus",
        "DASHSCOPE_BASE_URL": "https://dashscope.aliyuncs.com/compatible-mode/v1"
      }
    }
  }
}

如果之后修改了源码，一般不需要重新 link；重启 MCP client 即可加载新代码。

取消本地链接：

npm unlink -g image-mcp

方式二：npm pack

如果想模拟正式 npm 包安装，但仍然只在本地使用：

npm install
npm pack
npm install -g ./image-mcp-1.0.0.tgz

安装后同样可以在 MCP 配置中使用：

{
  "mcpServers": {
    "image-mcp": {
      "command": "image-mcp",
      "args": [],
      "env": {
        "DASHSCOPE_API_KEY": "your-api-key",
        "VISION_MODEL": "qwen-vl-plus"
      }
    }
  }
}

升级本地包时重新执行：

npm pack
npm install -g ./image-mcp-1.0.0.tgz

检查命令

确认全局命令可用：

which image-mcp
image-mcp

image-mcp 是 MCP stdio server，直接运行后会等待 MCP client 输入；没有输出不代表失败。实际验证建议在 MCP client 中查看是否能列出 recognize_image 工具。

特殊说明

项目扩展自 : https://github.com/asuojun/claude-vision-skill/tree/master

Available Tools

1 tool
recognize_imageRecognize imageA

识别本地或远程图片内容，默认使用 XML 形式输出，帮助 AI 理解图片内容。

ParametersJSON Schema
NameRequiredDescriptionDefault
promptNo可选识图提示词。默认要求 XML 输出。
image_urlNo远程图片 URL。与 image_path 二选一。
image_pathNo本地图片路径。与 image_url 二选一。
max_tokensNo最大输出 token 数，默认 1024。

TDQS

A3.8/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds behavioral context by mentioning the default XML output format and the ability to handle local or remote images, which is not fully disclosed by the schema alone. However, it does not disclose any side effects, permissions, or limitations, and with no annotations the description carries the full burden for behavioral transparency, leaving notable gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence in Chinese that is front-loaded with the main verb and resource. It avoids redundancy and every phrase contributes meaningful information about the tool's functionality and default behavior.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the core purpose, the input scope (local/remote), and the default output format (XML), which is particularly important since there is no output schema. It does not address return details, error handling, or constraints on input size, but for a simple recognition tool with 4 parameters and no siblings, the essential context is present.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, so all parameters are already documented with descriptions. The tool description adds no significant parameter semantics beyond what the schema provides, though it does implicitly reference local/remote sources which aligns with image_path and image_url.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function with a specific verb '识别' (recognize) and resource '图片内容' (image content), and specifies both local and remote sources. It is concise and unambiguous, and with no sibling tools present, there is no need for differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The purpose of the tool is implied ('帮助 AI 理解图片内容' – to help AI understand image content), which gives context for when to use it. However, it provides no explicit guidance on when not to use it or which alternative tools might be preferred, so it only partially covers usage guidelines.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 1 tool updatev1.0.0
    • First observedrecognize_image
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TDQS

A4/5.0
Disambiguation5/5

Only one tool exists, so there is no possibility of confusion between tools. The single recognize_image tool has a clear and distinct purpose.

Naming Consistency5/5

The tool name recognize_image follows a clear verb_noun pattern, which is descriptive and predictable. With only one tool, the naming convention is inherently consistent.

Tool Count3/5

Having a single tool feels thin for an image-related server. While the tool covers the core recognition function, the server lacks any additional utilities, making it borderline in scope.

Completeness5/5

The server's stated purpose is to help AI understand image content, and recognize_image fulfills this completely. There are no obvious missing operations within this narrow domain.

Maintenance

ActivityMaintained
ResponsivenessSyncing

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