Skip to main content
Glama
enesjakozh
GongRzhe

Image Generation MCP Server

by GongRzhe
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

이미지 생성 MCP 서버

이 MCP 서버는 Replicate Flux 모델을 사용하여 이미지 생성 기능을 제공합니다.

설치

옵션 1: NPX 방식(로컬 설정 필요 없음)

로컬에 설치하지 않고도 npm에서 바로 패키지를 사용할 수 있습니다.

지엑스피1

옵션 2: 로컬 설치

로컬 설치를 선호하는 경우:

# Global installation
npm install -g @gongrzhe/image-gen-server

# Or local installation
npm install @gongrzhe/image-gen-server

Related MCP server: FLUX MCP Server

설정

Claude Desktop 구성

Claude Desktop 구성 파일을 편집하세요.

  • MacOS의 경우: ~/Library/Application Support/Claude/claude_desktop_config.json

  • Windows의 경우: %APPDATA%/Claude/claude_desktop_config.json

옵션 1: NPX 구성(권장)

이 방법은 로컬 파일이 필요 없이 npm에서 직접 서버를 실행합니다.

{
  "mcpServers": {
    "image-gen": {
      "command": "npx",
      "args": ["@gongrzhe/image-gen-server"],
      "env": {
        "REPLICATE_API_TOKEN": "your-replicate-api-token",
        "MODEL": "alternative-model-name"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

옵션 2: 로컬 설치 구성

패키지를 로컬로 설치한 경우:

{
  "mcpServers": {
    "image-gen": {
      "command": "node",
      "args": ["/path/to/image-gen-server/build/index.js"],
      "env": {
        "REPLICATE_API_TOKEN": "your-replicate-api-token",
        "MODEL": "alternative-model-name"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

복제 API 토큰 받기

  1. https://replicate.com 에서 가입/로그인하세요

  2. https://replicate.com/account/api-tokens 로 이동하세요

  3. 새로운 API 토큰을 생성하세요

  4. 토큰을 복사하고 MCP 설정에서 your-replicate-api-token 교체하세요.

영상

환경 변수

  • REPLICATE_API_TOKEN (필수): 인증을 위한 Replicate API 토큰

  • MODEL (선택 사항): 이미지 생성에 사용할 Replicate 모델입니다. 기본값은 "black-forest-labs/flux-schnell"입니다.

구성 매개변수

  • disabled : 서버가 활성화( false )되어 있는지 또는 비활성화( true )되어 있는지 제어합니다.

  • autoApprove : 사용자 확인 없이 실행할 수 있는 도구 이름 배열입니다. 배열이 비어 있으면 모든 도구 호출에 확인이 필요합니다.

사용 가능한 도구

생성_이미지

텍스트 프롬프트를 기반으로 Flux 모델을 사용하여 이미지를 생성합니다.

영상

아웃-0 (1)

매개변수

  • prompt (필수): 생성할 이미지에 대한 텍스트 설명

  • seed (선택 사항): 재현 가능한 생성을 위한 무작위 시드

  • aspect_ratio (선택 사항): 이미지 종횡비(기본값: "1:1")

  • output_format (선택 사항): 출력 형식 - "webp", "jpg" 또는 "png"(기본값: "webp")

  • num_outputs (선택 사항): 생성할 이미지 수(1-4, 기본값: 1)

사용 예

const result = await use_mcp_tool({
  server_name: "image-gen",
  tool_name: "generate_image",
  arguments: {
    prompt: "A beautiful sunset over mountains",
    aspect_ratio: "16:9",
    output_format: "png",
    num_outputs: 1
  }
});

이 도구는 생성된 이미지에 대한 URL 배열을 반환합니다.

📜 라이센스

이 프로젝트는 MIT 라이선스에 따라 라이선스가 부여되었습니다.

Available Tools

1 tool
generate_imageC

Generate an image using the Flux model

NameRequiredDescriptionDefault
promptYesPrompt for generated image
seedNoRandom seed for reproducible generation
aspect_ratioNoAspect ratio for the generated image1:1
output_formatNoFormat of the output imageswebp
num_outputsNoNumber of outputs to generate (1-4)

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 action ('generate') but doesn't mention cost, rate limits, permissions, or output behavior (e.g., file format, size). This is a significant gap for a generative tool with potential resource implications.

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, efficient sentence with zero waste. It's front-loaded with the core action and model, making it easy to parse quickly. Every word earns its place without redundancy.

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 generation, no annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like cost, rate limits, or output details (e.g., image size, quality), leaving gaps for the agent to operate effectively.

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?

Schema description coverage is 100%, so the schema fully documents all parameters. The description adds no additional meaning beyond the schema, such as explaining prompt best practices or seed usage. Baseline 3 is appropriate when 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 clearly states the verb ('generate') and resource ('image'), specifying the model ('Flux model'). It's specific about what the tool does, though without sibling tools, differentiation isn't applicable. It's not tautological or misleading.

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?

No guidance is provided on when to use this tool versus alternatives, prerequisites, or constraints. The description lacks context for usage, such as when image generation is appropriate or any limitations, leaving the agent without operational guidance.

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. 1 tool updatev1.0.1
    • First observedgenerate_image
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

B3/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool has a clear and distinct purpose of generating images using the Flux model.

Naming Consistency5/5

Since there is only one tool, naming consistency is inherently perfect. The tool name 'generate_image' follows a clear verb_noun pattern, and there are no other tools to cause inconsistency.

Tool Count2/5

A single tool for an image generation server is too few for the apparent scope, as it lacks basic operations like listing models, managing generations, or handling variations. This minimal set will likely cause agent failures due to incomplete functionality.

Completeness1/5

The tool surface is severely incomplete for an image generation domain. It only provides generation without any supporting operations such as model selection, parameter tuning, history retrieval, or image editing, leading to dead ends in agent workflows.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Related MCP Connectors

Related MCP Servers

Appeared in Searches