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GongRzhe

Image Generation MCP Server

by GongRzhe
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

图像生成 MCP 服务器

该 MCP 服务器使用 Replicate Flux 模型提供图像生成功能。

安装

选项 1：NPX 方法（无需本地设置）

您可以直接从 npm 使用该包，而无需在本地安装它：

# No installation needed - npx will handle it

选项 2：本地安装

如果您更喜欢本地安装：

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

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

Related MCP server: FLUX MCP Server

设置

配置 Claude 桌面

编辑您的 Claude Desktop 配置文件：

  • 在 MacOS 上： ~/Library/Application Support/Claude/claude_desktop_config.json

  • 在 Windows 上： %APPDATA%/Claude/claude_desktop_config.json

选项 1：NPX 配置（推荐）

此方法直接从 npm 运行服务器，而不需要本地文件：

{
  "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": []
    }
  }
}

选项 2：本地安装配置

如果您在本地安装了该包：

{
  "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": []
    }
  }
}

获取您的复制 API 令牌

  1. 在https://replicate.com注册/登录

  2. 前往https://replicate.com/account/api-tokens

  3. 创建新的 API 令牌

  4. 复制令牌并替换 MCP 设置中的your-replicate-api-token

图像

环境变量

  • REPLICATE_API_TOKEN （必需）：用于身份验证的复制 API 令牌

  • MODEL （可选）：用于图像生成的 Replicate 模型。默认为“black-forest-labs/flux-schnell”。

配置参数

  • disabled ：控制服务器是否启用（ false ）或禁用（ true ）

  • autoApprove ：无需用户确认即可执行的工具名称数组。空数组表示所有工具调用均需用户确认。

可用工具

生成图像

根据文本提示，使用 Flux 模型生成图像。

图像

出-0 (1)

参数

  • prompt （必需）：要生成的图像的文本描述

  • seed （可选）：用于可重复生成的随机种子

  • aspect_ratio （可选）：图像纵横比（默认值：“1：1”）

  • output_format （可选）：输出格式 - “webp”、“jpg”或“png”（默认值：“webp”）

  • num_outputs （可选）：要生成的图像数量（1-4，默认值：1）

示例用法

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
  }
});

该工具返回所生成图像的 URL 数组。

📜 许可证

该项目已获得 MIT 许可。

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

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