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Image Processor MCP Server

by sanliunanjue
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JavaScript

图像处理器 MCP 服务器

模型上下文协议服务器

这是一个基于 TypeScript 的 MCP 服务器，实现了一个简单的笔记系统。它通过提供以下内容来演示 MCP 的核心概念：

  • 用 URI 和元数据表示文本注释的资源

  • 创建新笔记的工具

  • 生成笔记摘要的提示

特征

资源

  • 通过note:// URI 列出并访问笔记

  • 每个笔记都有标题、内容和元数据

  • 用于简单内容访问的纯文本 MIME 类型

工具

  • create_note创建新的文本注释

    • 将标题和内容作为必需参数

    • 将注释存储在服务器状态中

提示

  • summarize_notes - 生成所有存储笔记的摘要

    • 包含所有笔记内容作为嵌入资源

    • 返回 LLM 摘要的结构化提示

Related MCP server: azure-devops MCP Server

发展

安装依赖项：

npm install

构建服务器：

npm run build

对于使用自动重建的开发：

npm run watch

安装

要与 Claude Desktop 一起使用，请添加服务器配置：

在 MacOS 上： ~/Library/Application Support/Claude/claude_desktop_config.json在 Windows 上： %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "image-processor": {
      "command": "/path/to/image-processor/build/index.js"
    }
  }
}

调试

由于 MCP 服务器通过 stdio 进行通信，调试起来可能比较困难。我们推荐使用MCP Inspector ，它以包脚本的形式提供：

npm run inspector

检查器将提供一个 URL 来访问浏览器中的调试工具。

Available Tools

2 tools
process_image_to_codeC

处理图像并生成代码

ParametersJSON Schema
NameRequiredDescriptionDefault
image_urlYes图像URL
languageNo目标编程语言，例如：python, javascript, html, css等python
instructionsNo额外的指令或要求

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool processes images and generates code, but doesn't explain how it handles different image types, what the output format is (e.g., code snippet, full file), error handling, or any limitations (e.g., image size, supported languages beyond the schema). This leaves significant gaps in understanding the tool's behavior.

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 extremely concise with a single sentence '处理图像并生成代码' that directly states the tool's function. It's front-loaded with no wasted words, making it efficient and easy to parse. Every word earns its place by conveying essential information.

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

Completeness2/5

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

Given the complexity of image-to-code conversion, no annotations, and no output schema, the description is insufficiently complete. It doesn't address what the output looks like (e.g., code structure, error messages), how it handles ambiguous images, or any prerequisites (e.g., image format requirements). For a tool with three parameters and no structured output documentation, more context is needed.

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%, with clear descriptions for all three parameters in the input schema. The description doesn't add any additional meaning beyond what's already documented in the schema (e.g., it doesn't explain how 'instructions' modify code generation or provide examples). Given the high schema coverage, the baseline score of 3 is appropriate as the schema does the heavy lifting.

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

Purpose4/5

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

The description '处理图像并生成代码' clearly states the tool's purpose: processing images and generating code. It specifies both the verb ('处理' - process) and resource ('图像' - image) with the output ('代码' - code). However, it doesn't differentiate from its sibling 'process_image_to_description', which suggests a similar input but different output type.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. There's no mention of the sibling tool 'process_image_to_description' or any context about when code generation is preferred over description generation. It lacks explicit usage scenarios or exclusions.

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

process_image_to_descriptionC

处理图像并生成描述

ParametersJSON Schema
NameRequiredDescriptionDefault
image_urlYes图像URL
detail_levelNo描述详细程度：简洁(brief)、标准(standard)或详细(detailed)standard
focusNo描述重点，例如：物体、场景、人物、情感等

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool processes images and generates descriptions, but doesn't reveal important behavioral aspects like whether this is a read-only operation, what permissions might be required, potential rate limits, error conditions, or what format the description output takes. For a tool with no annotation coverage, this leaves significant gaps in understanding how the tool behaves.

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 extremely concise - a single Chinese sentence that directly states the tool's function. There's no wasted language or unnecessary elaboration. It's front-loaded with the core purpose and doesn't include any extraneous information.

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

Completeness2/5

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

For a tool with 3 parameters, no annotations, and no output schema, the description is insufficiently complete. While concise, it doesn't address the behavioral aspects needed when annotations are absent, doesn't explain the relationship between parameters, and provides no information about the output format or structure. The description should do more to compensate for the lack of structured metadata.

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 description adds no parameter information beyond what's already in the schema, which has 100% coverage. All three parameters (image_url, detail_level, focus) are fully documented in the schema with descriptions, enums, defaults, and requirements. The description doesn't provide additional context about parameter usage, relationships, or examples, so it meets the baseline for high schema coverage.

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

Purpose4/5

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

The description clearly states the tool's purpose: '处理图像并生成描述' (process image and generate description). It specifies both the action (process) and resource (image) with a clear output (description). However, it doesn't explicitly differentiate from its sibling tool 'process_image_to_code', which processes images but generates code instead of descriptions.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. There's no mention of the sibling tool 'process_image_to_code' or any other context for choosing between image processing tools. The description simply states what the tool does without indicating appropriate use cases or exclusions.

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.

  1. 2 tool updates
    • First observedprocess_image_to_code
    • First observedprocess_image_to_description
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.1/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: one generates code from images, and the other generates descriptions from images. There is no overlap in functionality, making it easy for an agent to choose the correct tool based on the desired output type.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern with 'process_image_to_' as a prefix, followed by the specific output type ('code' or 'description'). This naming scheme is predictable and enhances readability across the tool set.

Tool Count2/5

With only two tools, the server feels thin for an 'Image Processor' domain, which might imply broader capabilities like image editing, filtering, or analysis. The limited scope suggests potential gaps in functionality that could hinder agent workflows.

Completeness2/5

The tool set is severely incomplete for an image processing domain. It lacks basic operations such as image resizing, format conversion, filtering, or analysis tools, focusing only on generation tasks. This creates significant gaps that will likely cause agent failures when broader image processing needs arise.

Maintenance

ActivityInactive
ResponsivenessNo issues

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