mcp-4o-Image-Generator
4o-이미지 MCP 서버
4o-image API와 통합되는 MCP 서버 구현으로, LLM 및 기타 AI 시스템이 표준화된 프로토콜을 통해 이미지를 생성하고 편집할 수 있도록 지원합니다. 간단한 텍스트 프롬프트를 사용하여 고품질 아트, 3D 캐릭터 및 맞춤형 이미지를 제작하세요.
특징
텍스트-이미지 생성 : AI를 사용하여 텍스트 설명에서 이미지 생성
이미지 편집 : 텍스트 프롬프트를 사용하여 기존 이미지 변환
실시간 진행 상황 업데이트 : 생성 상태에 대한 피드백을 받으세요
브라우저 통합 : 생성된 이미지를 기본 브라우저에서 자동으로 엽니다.
Related MCP server: image-forge-mcp
도구
이미지 생성
선택적 이미지 편집을 통해 텍스트 프롬프트를 기반으로 이미지 생성
입력:
prompt(문자열, 필수): 원하는 이미지에 대한 텍스트 설명imageBase64(문자열, 선택 사항): 편집 또는 스타일 전송을 위한 Base64로 인코딩된 이미지
구성
API 키 받기
4o-image.app 에서 계정을 등록하세요
사용자 대시보드에서 API 키를 얻으세요
서버를 실행할 때 API 키를 환경 변수로 설정합니다.
Claude Desktop과 함께 사용
claude_desktop_config.json 에 다음을 추가하세요:
지엑스피1
사용 예
다음은 Claude와 함께 이 MCP 서버를 사용하는 예입니다.
Generate an image of a dog running on the beach at sunsetClaude는 MCP 서버를 사용하여 이미지를 생성하며, 이미지는 기본 브라우저에서 자동으로 열립니다. Claude의 응답에는 이미지로 바로 연결되는 링크도 포함되어 있습니다.
이미지 편집을 위해 기본 이미지를 포함하고 Claude에게 수정하도록 요청할 수 있습니다.
Edit this image to make the sky more dramatic with storm clouds특허
이 MCP 서버는 MIT 라이선스에 따라 라이선스가 부여됩니다. MIT 라이선스의 약관에 따라 소프트웨어를 자유롭게 사용, 수정 및 배포할 수 있습니다.
Available Tools
1 toolgenerateImageA
Generate images using the 4o-image API and automatically open the results in your browser.
This tool generates images based on your prompt and automatically opens them in your default browser, while also returning a clickable link.
The tool supports two modes:
Text-to-image - Create new images using just a text prompt
Image editing - Provide a base image and prompt for editing or style transfer
The response will include a direct link to the generated image and detailed information.
Visit our website: https://4o-image.app/
| Name | Required | Description | Default |
|---|---|---|---|
| imageBase64 | No | Optional base image (Base64 encoded) for image editing or upscaling | |
| prompt | Yes | Text description of the desired image content |
TDQS
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 adds valuable context beyond the input schema by describing automatic browser opening, return of a clickable link, and support for two modes. However, it does not cover important behavioral traits such as rate limits, authentication needs, error handling, or response format details, leaving gaps for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded, starting with the core functionality and then detailing modes and responses. Most sentences add value, but the final promotional sentence ('Visit our website...') is extraneous and does not aid tool selection or invocation, slightly reducing efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (image generation with two modes), no annotations, and no output schema, the description is moderately complete. It covers purpose, usage modes, and some behavioral aspects (browser opening, link return), but lacks details on output structure, error cases, or operational constraints, which are important for a tool without structured output documentation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema already documents both parameters (imageBase64 and prompt) adequately. The description adds marginal value by explaining the two modes that correspond to these parameters, but it does not provide additional syntax, format, or constraint details beyond what the schema states. This 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate images using the 4o-image API' and specifies it 'automatically opens the results in your browser.' It distinguishes between text-to-image and image editing modes, providing specific functionality details. However, without sibling tools, differentiation from alternatives is not applicable, preventing a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage context by outlining two modes (text-to-image and image editing) and indicating when to use each based on whether an imageBase64 parameter is provided. It mentions that the tool opens results in the browser and returns a clickable link, offering practical guidance. However, it lacks explicit exclusions or comparisons to alternatives, as no sibling tools exist.
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 tool update
v1.0.0- First observed
generateImage
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'generateImage' has a clear, distinct purpose for image generation and editing.
The tool name 'generateImage' follows a consistent verb_noun pattern, and with only one tool, there is no inconsistency to evaluate. The naming is clear and appropriate for its function.
A single tool is too few for the server's purpose of image generation and editing, as it lacks coverage for related operations like listing generated images, managing settings, or handling errors. This minimal set limits agent functionality and feels incomplete for the domain.
The tool surface is severely incomplete for an image generation server. While 'generateImage' covers creation and editing, there are obvious gaps such as no tools for retrieving past images, deleting images, or configuring generation parameters, which are essential for a full workflow.
Maintenance
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