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Image Processor MCP Server

by sanliunanjue
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript

이미지 프로세서 MCP 서버

모델 컨텍스트 프로토콜 서버

간단한 메모 시스템을 구현하는 TypeScript 기반 MCP 서버입니다. 다음을 제공하여 핵심 MCP 개념을 보여줍니다.

  • URI 및 메타데이터를 사용하여 텍스트 노트를 나타내는 리소스

  • 새로운 노트를 만드는 도구

  • 노트 요약 생성을 위한 프롬프트

특징

자원

  • note:// URI를 통해 메모를 나열하고 액세스합니다.

  • 각 노트에는 제목, 내용 및 메타데이터가 있습니다.

  • 간단한 콘텐츠 액세스를 위한 일반 텍스트 MIME 유형

도구

  • create_note - 새로운 텍스트 노트 만들기

    • 제목과 내용을 필수 매개변수로 사용합니다.

    • 서버 상태에 메모 저장

프롬프트

  • summarize_notes - 저장된 모든 노트의 요약을 생성합니다.

    • 모든 노트 내용을 내장 리소스로 포함합니다.

    • LLM 요약을 위한 구조화된 프롬프트를 반환합니다.

Related MCP server: azure-devops MCP Server

개발

종속성 설치:

지엑스피1

서버를 빌드하세요:

npm run build

자동 재빌드를 사용한 개발의 경우:

npm run watch

설치

Claude Desktop과 함께 사용하려면 서버 구성을 추가하세요.

MacOS의 경우: ~/Library/Application Support/Claude/claude_desktop_config.json Windows의 경우: %APPDATA%/Claude/claude_desktop_config.json

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

디버깅

MCP 서버는 stdio를 통해 통신하므로 디버깅이 어려울 수 있습니다. 패키지 스크립트로 제공되는 MCP Inspector를 사용하는 것이 좋습니다.

npm run inspector

검사기는 브라우저에서 디버깅 도구에 액세스할 수 있는 URL을 제공합니다.

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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