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image-gen3-google-mcp-server

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Gemini Imagen 3.0 MCP Server

License Node TypeScript

A professional Model Context Protocol (MCP) server implementation that harnesses Google's Imagen 3.0 model through the Gemini API for high-quality image generation. Built with TypeScript and designed for seamless integration with Claude Desktop and other MCP-compatible hosts.

🌟 Features

  • Leverage Google's state-of-the-art Imagen 3.0 model via Gemini API

  • Generate up to 4 high-quality images per request

  • Automatic file management with intelligent naming

  • HTML preview generation with file:// protocol support

  • Built on MCP protocol for AI agent compatibility

  • TypeScript implementation with robust error handling

Related MCP server: Nano Banana MCP Server

🚀 Quick Start

Prerequisites

  • Node.js 18 or higher

  • Google Gemini API key

  • Claude Desktop or another MCP-compatible host

Installation

  1. Clone the repository:

git clone https://github.com/yourusername/gemini-imagen-mcp-server.git
cd gemini-imagen-mcp-server

  1. Install dependencies:

npm install

  1. Build the TypeScript code:

npm run build

⚙️ Configuration

  1. Configure Claude Desktop by adding to claude_desktop_config.json:

{
  "mcpServers": {
    "gemini-image-gen": {
      "command": "node",
      "args": ["./build/index.js"],
      "cwd": "<path-to-project-directory>",
      "env": {
        "GEMINI_API_KEY": "your-gemini-api-key"
      }
    }
  }
}

  1. Replace placeholders:

    • <path-to-project-directory>: Your project path

    • your-gemini-api-key: Your Gemini API key

🛠️ Available Tools

1. generate_images

Generates images using Google's Imagen 3.0 model.

Parameters:

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

  • numberOfImages (optional): Number of images (1-4, default: 1)

File Management:

  • Images are automatically saved in G:\image-gen3-google-mcp-server\images

  • Filenames follow the pattern: {sanitized-prompt}-{timestamp}-{index}.png

  • Timestamps ensure unique filenames

  • Prompts are sanitized for safe filesystem usage

Example:

Generate an image of a futuristic city at night

2. create_image_html

Creates HTML preview tags for generated images.

Parameters:

  • imagePaths (required): Array of image file paths

  • width (optional): Image width in pixels (default: 512)

  • height (optional): Image height in pixels (default: 512)

Returns HTML tags with absolute file:// URLs for local viewing.

Example:

Create HTML tags for the generated images with width=400

🔧 Development

# Install dependencies
npm install

# Build TypeScript
npm run build

# Run tests (when available)
npm test

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes:

  1. Fork the repository

  2. Create your feature branch (git checkout -b feature/AmazingFeature)

  3. Commit your changes (git commit -m 'Add some AmazingFeature')

  4. Push to the branch (git push origin feature/AmazingFeature)

  5. Open a Pull Request

📝 Error Handling

The server implements two main error codes:

  • tool_not_found (1): When the requested tool is not available

  • execution_error (2): When image generation or HTML creation fails

📄 License

MIT License - see the LICENSE file for details.

✨ Author

Falah G. Salieh

🙏 Acknowledgments

  • Google Gemini API and Imagen 3.0 model

  • Model Context Protocol (MCP) by Anthropic

  • Claude Desktop team for MCP host implementation

📌 Tags

#MCP #Gemini #Imagen3 #AI #ImageGeneration #TypeScript #NodeJS #GoogleAI #ClaudeDesktop

Made with ❤️ by Falah G. Salieh

Available Tools

2 tools
create_image_htmlB

Create HTML img tags from image file paths

ParametersJSON Schema
NameRequiredDescriptionDefault
imagePathsYesArray of image file paths
widthNoImage width in pixels
heightNoImage height in pixels

TDQS

B3/5.0
Behavior1/5

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

With no annotations, the description should disclose behavioral traits but does not. It omits details about error handling, output format, or side effects, leaving the agent uninformed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence with no waste. It is concise but could be slightly more informative without sacrificing brevity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with 3 parameters and no output schema, the description gives the basic idea but lacks details such as tag format, handling of missing files, or default attributes. It is adequate but not thorough.

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 coverage is 100%, so baseline is 3. The description adds no extra parameter information beyond what the schema already provides.

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 it creates HTML img tags from file paths. It distinguishes from sibling tool generate_images by focusing on HTML output rather than image generation.

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 the sibling generate_images. No context on prerequisites or appropriate scenarios is given.

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

generate_imagesB

Generate images using Google Gemini AI

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYesText description of the image to generate
numberOfImagesNoNumber of images to generate (1-4)
outputDirNoDirectory to save generated imagesG:\image-gen3-google-mcp-server\images

TDQS

B3/5.0
Behavior2/5

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

With no annotations, the description carries full responsibility for behavioral disclosure. It only states that images are generated, but omits key traits such as whether the tool saves files to disk (though implied by 'outputDir' in schema), cost implications, rate limits, or error behavior. The agent learns little beyond the basic purpose.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence achieving conciseness, but it lacks structure and front-loading of critical constraints. It does not highlight the image count range (1-4) or output directory default, which are important for usage but are buried in the schema.

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?

The tool has no output schema, so the description should explain what the tool returns (e.g., file paths, base64 data). It does not. Given the sibling tool suggesting alternative image-related functionality, more context on output format and typical use cases would be beneficial.

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 coverage is 100%, so the schema already documents all parameters. The description adds the context of using 'Google Gemini AI', which is not in the schema. However, it does not clarify or enrich the parameter meanings beyond the schema descriptions. Baseline of 3 is appropriate.

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 action ('generate images') and specifies the service ('Google Gemini AI'). It effectively distinguishes from the sibling 'create_image_html' tool by indicating this generates actual images rather than HTML.

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 the sibling 'create_image_html'. There are no conditions, prerequisites, or exclusions mentioned, leaving the agent to infer context from the tool name alone.

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. 2 tool updatesv1.0.0
    • First observedcreate_image_html
    • First observedgenerate_images
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TDQS

B3.2/5.0
Disambiguation5/5

Each tool targets a completely different operation: generate_images creates new images via AI, while create_image_html outputs HTML for existing image files. There is no ambiguity or overlap between them.

Naming Consistency5/5

Both tools follow a clear verb_noun pattern in snake_case. 'generate_images' and 'create_image_html' use consistent styling and predictable naming conventions.

Tool Count3/5

Only 2 tools for an image generation service is borderline. While the core functionality (generation and HTML output) is covered, the small number suggests a limited scope that may require additional tools for a complete workflow.

Completeness2/5

The tool set is notably incomplete: it lacks tools for listing, deleting, or managing generated images, and there is no way to configure generation parameters beyond what might be in the description. Users are left with a generation-and-output loop without lifecycle management.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

Unclaimed servers have limited discoverability.

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If you are the server author, to access and configure the admin panel.

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  • Gemini Imagen 3.0 MCP Server

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