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IA-Programming

MCP Image Server

by IA-Programming
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

MCP 서버 - 이미지

URL, 로컬 파일 경로 및 numpy 배열에서 이미지를 가져오고 처리하는 도구를 제공하는 모델 컨텍스트 프로토콜(MCP) 서버입니다. 이 서버에는 이미지를 MIME 유형과 함께 base64로 인코딩된 문자열로 반환하는 fetch_images라는 도구가 포함되어 있습니다.

우리를 지원해주세요

이 프로젝트가 도움이 되셨고 향후 프로젝트를 후원하고 싶으시다면, 커피 한 잔 어떠세요? 여러분의 후원은 저희가 혁신적인 AI 솔루션을 지속적으로 개발하는 데 큰 도움이 됩니다.

여러분의 기여는 지능적이고 사용자 친화적인 애플리케이션을 만드는 우리의 열정에 큰 도움이 됩니다.

Related MCP server: Image Toolkit MCP Server

목차

특징

  • URL(http/https)에서 이미지 가져오기

  • 로컬 파일 경로에서 이미지 로드

  • 대규모 로컬 이미지에 대한 특수 처리

  • 대용량 이미지(>1MB)에 대한 자동 이미지 압축

  • 여러 이미지의 병렬 처리

  • 다양한 파일 확장자에 대한 적절한 MIME 유형 매핑

  • 포괄적인 오류 처리 및 로깅

필수 조건

  • 파이썬 3.10+

  • uv 패키지 관리자(추천)

설치

  1. 이 저장소를 복제하세요

  2. uv를 사용하여 가상 환경을 만들고 활성화합니다.

지엑스피1

  1. uv를 사용하여 종속성을 설치합니다.

uv pip install -r requirements.txt

서버 실행

MCP 서버를 실행하는 방법은 두 가지가 있습니다.

1. 직접법

MCP 서버를 직접 시작하려면:

uv run python mcp_image.py

2. Windsurf/Cursor 구성

윈드서핑

Windsurf에 이 MCP 서버를 추가하려면:

  1. ~/.codeium/windsurf/mcp_config.json의 구성 파일을 편집하세요.

  2. 다음 구성을 추가합니다.

{
  "mcpServers": {
    "image": {
      "command": "uv",
        "args": ["--directory", "/path/to/mcp-image", "run", "mcp_image.py"]
    }
  }
}

커서

이 MCP 서버를 Cursor에 추가하려면:

  1. 커서를 열고 설정 (탐색 모음 → 커서 설정)으로 이동합니다.

  2. 기능 → MCP 서버 로 이동

    • 새 MCP 서버 추가를 클릭하세요

  4. 다음 구성을 입력하세요.

{
  "mcpServers": {
    "image": {
      "command": "uv",
      "args": ["--directory", "/path/to/mcp-image", "run", "mcp_image.py"]
    }
  }
}

사용 가능한 도구

서버는 다음과 같은 도구를 제공합니다.

fetch_images : URL 또는 로컬 파일 경로에서 이미지를 가져오고 처리합니다. 매개변수: image_sources: 이미지에 대한 URL 또는 파일 경로 목록 반환: base64 인코딩 및 MIME 유형이 포함된 처리된 이미지 목록

사용 예

이제 다음과 같은 명령을 사용할 수 있습니다.

  • "[URL 또는 파일 경로 목록]" 이미지를 가져옵니다.

  • "이 로컬 이미지를 로드하고 처리하세요: [file_path]"

예시

# URL-only test
[
  "https://upload.wikimedia.org/wikipedia/commons/thumb/7/70/Chocolate_%28blue_background%29.jpg/400px-Chocolate_%28blue_background%29.jpg",
  "https://imgs.search.brave.com/Sz7BdlhBoOmU4wZjnUkvgestdwmzOzrfc3GsiMr27Ik/rs:fit:860:0:0:0/g:ce/aHR0cHM6Ly9pbWdj/ZG4uc3RhYmxlZGlm/ZnVzaW9ud2ViLmNv/bS8yMDI0LzEwLzE4/LzJmOTY3NTViLTM0/YmQtNDczNi1iNDRh/LWJlMTVmNGM5MDBm/My5qcGc",
  "https://shigacare.fukushi.shiga.jp/mumeixxx/img/main.png"
]

# Mixed URL and local file test
[
  "https://upload.wikimedia.org/wikipedia/commons/thumb/7/70/Chocolate_%28blue_background%29.jpg/400px-Chocolate_%28blue_background%29.jpg",
  "C:\\Users\\username\\Pictures\\image1.jpg",
  "https://imgs.search.brave.com/Sz7BdlhBoOmU4wZjnUkvgestdwmzOzrfc3GsiMr27Ik/rs:fit:860:0:0:0/g:ce/aHR0cHM6Ly9pbWdj/ZG4uc3RhYmxlZGlm/ZnVzaW9ud2ViLmNv/bS8yMDI0LzEwLzE4/LzJmOTY3NTViLTM0/YmQtNDczNi1iNDRh/LWJlMTVmNGM5MDBm/My5qcGc",
  "C:\\Users\\username\\Pictures\\image2.jpg"
]

디버깅

문제가 발생할 경우:

  1. 모든 종속성이 올바르게 설치되었는지 확인하세요

  2. 서버가 실행 중이고 연결을 수신하고 있는지 확인하세요.

  3. 로컬 이미지 로딩 문제의 경우 파일 경로가 올바르고 액세스 가능한지 확인하세요.

  4. "지원되지 않는 이미지 유형" 오류의 경우 콘텐츠 유형 처리를 확인하십시오.

  5. 서버 출력에서 오류 메시지를 찾아보세요.

기여하다

기여를 환영합니다! 풀 리퀘스트를 제출해 주세요.

특허

이 프로젝트는 MIT 라이선스에 따라 라이선스가 부여되었습니다. 자세한 내용은 라이선스 파일을 참조하세요.

Available Tools

1 tool
fetch_imagesA
Fetch and process images from URLs or local file paths, returning them in a format suitable for LLMs.

This tool accepts a list of image sources which can be either:
1. URLs pointing to web-hosted images (http:// or https://)
2. Local file paths pointing to images stored on the local filesystem (e.g., "C:/images/photo1.jpg")

For a single image, provide a one-element list. The function will process images in parallel
when multiple sources are provided. Images that exceed the size limit (1MB) will be automatically 
compressed while maintaining aspect ratio and reasonable quality.

Args:
    image_sources: A list of image URLs or local file paths. For a single image, provide a one-element list.
    
Returns:
    A list of Image objects or None values (if processing failed) in the same order as the input sources.
ParametersJSON Schema
NameRequiredDescriptionDefault
image_sourcesYes

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden and discloses key behavioral traits: parallel processing for multiple images, automatic compression for images over 1MB with aspect ratio and quality preservation, and failure handling (returns None for failed processing). It doesn't cover aspects like rate limits or authentication needs, but provides substantial operational context.

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 well-structured and front-loaded with the core purpose, followed by detailed input specifications, processing behavior, and return values. Every sentence adds value without redundancy, and it's appropriately sized for the tool's complexity.

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?

Given the tool's moderate complexity (1 parameter, no output schema, no annotations), the description is largely complete: it covers purpose, input semantics, processing behavior, and return format. However, it lacks details on the 'Image objects' structure (e.g., format, metadata) and any error specifics, which would enhance completeness for an agent.

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

Parameters5/5

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

The schema description coverage is 0%, so the description must compensate fully. It clearly explains the single parameter 'image_sources' as a list of URLs or file paths, specifies format examples (http/https URLs, local paths like 'C:/images/photo1.jpg'), and clarifies handling for single images (one-element list). This adds comprehensive meaning beyond the bare schema.

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 purpose with specific verbs ('fetch and process images') and resources ('from URLs or local file paths'), and distinguishes its output format ('suitable for LLMs'). With no sibling tools, it fully defines its scope without redundancy.

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 description implies usage by specifying input types (URLs or file paths) and handling of single vs. multiple images, but lacks explicit guidance on when to use this tool versus alternatives (e.g., other image tools or direct file handling). With no siblings, this is less critical but still a gap.

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

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TDQS

A4.1/5.0
Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'fetch_images' has a clearly defined and distinct purpose that cannot be confused with any other tool in this server.

Naming Consistency5/5

The single tool name 'fetch_images' follows a clear verb_noun pattern, and with only one tool, there is perfect consistency. No naming conventions can conflict when only one tool exists.

Tool Count2/5

A single tool is generally too few for most server purposes, creating a thin surface that limits functionality. While this tool handles image fetching and processing well, the server's scope as an 'Image Server' suggests potential gaps that would require additional tools for comprehensive image operations.

Completeness2/5

For an 'Image Server' domain, having only a fetch/processing tool leaves significant gaps. There are no tools for image manipulation (resize, crop, filter), analysis (object detection, metadata extraction), or management (list, delete, organize images), making the surface severely incomplete for typical image-related workflows.

Maintenance

ActivityInactive
ResponsivenessNo issues

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