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GongRzhe

Image Generation MCP Server

by GongRzhe
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JavaScript
Remote

Image Generation MCP Server

This MCP server provides image generation capabilities using the Replicate Flux model.

Installation

Installing via Smithery

To install Image Generation MCP Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @GongRzhe/Image-Generation-MCP-Server --client claude

Option 1: NPX Method (No Local Setup Required)

You can use the package directly from npm without installing it locally:

# No installation needed - npx will handle it

Option 2: Local Installation

If you prefer a local installation:

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

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

Related MCP server: FLUX MCP Server

Setup

Configure Claude Desktop

Edit your Claude Desktop configuration file:

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

  • On Windows: %APPDATA%/Claude/claude_desktop_config.json

Option 1: NPX Configuration (Recommended)

This method runs the server directly from npm without needing local files:

{
  "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": []
    }
  }
}

Option 2: Local Installation Configuration

If you installed the package locally:

{
  "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": []
    }
  }
}

Get Your Replicate API Token

  1. Sign up/login at https://replicate.com

  2. Go to https://replicate.com/account/api-tokens

  3. Create a new API token

  4. Copy the token and replace your-replicate-api-token in the MCP settings

image

Environment Variables

  • REPLICATE_API_TOKEN (required): Your Replicate API token for authentication

  • MODEL (optional): The Replicate model to use for image generation. Defaults to "black-forest-labs/flux-schnell"

Configuration Parameters

  • disabled: Controls whether the server is enabled (false) or disabled (true)

  • autoApprove: Array of tool names that can be executed without user confirmation. Empty array means all tool calls require confirmation.

Available Tools

generate_image

Generates images using the Flux model based on text prompts.

image

out-0 (1)

Parameters

  • prompt (required): Text description of the image to generate

  • seed (optional): Random seed for reproducible generation

  • aspect_ratio (optional): Image aspect ratio (default: "1:1")

  • output_format (optional): Output format - "webp", "jpg", or "png" (default: "webp")

  • num_outputs (optional): Number of images to generate (1-4, default: 1)

Example Usage

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
  }
});

The tool returns an array of URLs to the generated images.

📜 License

This project is licensed under the MIT License.

Available Tools

1 tool
generate_imageC

Generate an image using the Flux model

ParametersJSON Schema
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. Dates show when Glama detected each change.

  1. 1 tool updatev1.0.1
    • First observedgenerate_image
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TDQS

B3/5.0
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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