Imagen3-MCP Details

Imagen3-MCP is an image generation service based on Google's Imagen 3.0 that exposes its functionality through MCP (Model Control Protocol). The project provides a server to run a local MCP service that accesses Google Gemini-powered image generation, enabling developers to integrate advanced image synthesis into their applications. The documentation covers prerequisites (Gemini API key), installation steps for Cherry Studio, and a Cursor-based JSON configuration example for embedding the MCP server in broader tooling. This MCP is designed to be deployment-friendly, with configurable environment variables and optional proxy settings to adapt to various network environments.

Use Case

Imagen3-MCP acts as a server that provides access to Google's Imagen 3.0 image generation via MCP. It is intended for developers who want to run a local or containerized image generator and expose it to their applications through MCP. The documentation shows how to configure the MCP server with a command, environment variables (notably GEMINI_API_KEY), and optional settings for proxying and server addresses. Example usage includes a Cursor-style JSON configuration that demonstrates how to structure the mcpServers block and environment variables for Gemini authentication and optional server customization.

Examples & Tutorials

{
"mcpServers": {
"imagen3": {
"command": "C:\\bin\\imagen3-mcp.exe",
"env": {
"GEMINI_API_KEY": "<GEMINI_API_KEY>"
// Optional environment variables:
// "BASE_URL": "<PROXY_URL>",
// "SERVER_LISTEN_ADDR": "0.0.0.0", // Example: Listen on all interfaces
// "SERVER_PORT": "9981",
// "IMAGE_RESOURCE_SERVER_ADDR": "your.domain.com" // Example: Use a domain name for image URLs
}
}
}
}

This demonstrates how to configure the MCP server in a Cursor-style JSON snippet as documented.

Installation Guide

安装步骤——Cherry Studio:

  • GitHub Releases 下载最新版本的可执行文件

  • 将下载的可执行文件放置在系统中的任意位置,例如 C:\bin\imagen3-mcp.exe

  • 在 Cherry Studio 中配置:

  • Command 字段填写可执行文件路径,例如 C:\bin\imagen3-mcp.exe

  • 环境变量 GEMINI_API_KEY 中填写你的 Gemini API 密钥

  • [可选] 环境变量 BASE_URL 中填写代理地址,例如 https://lingxi-proxy.hamflx.dev/api/provider/google(这个地址可以解决 GFW 的问题,但是解决不了 Google 对 IP 的限制问题,因此还是得挂梯子)。

  • [可选] 环境变量 SERVER_LISTEN_ADDR:设置服务器监听的 IP 地址(默认为 127.0.0.1)。

  • [可选] 环境变量 SERVER_PORT:设置服务器监听的端口和图片 URL 使用的端口(默认为 9981)。

  • [可选] 环境变量 IMAGE_RESOURCE_SERVER_ADDR:设置图片 URL 中使用的服务器地址(默认为 127.0.0.1)。这在服务器运行在容器或远程机器上时很有用。
  • Integration Guides

    Frequently Asked Questions

    Is this your MCP?

    Claim ownership and get verified badge

    Repository Stats
    Important Notes

    Requires a valid Google Gemini API key. Optional: BASE_URL for proxy to bypass GFW, but it does not solve Google IP restrictions. The BASE_URL example shows how to proxy API requests. In addition, there are optional env vars for SERVER_LISTEN_ADDR, SERVER_PORT, and IMAGE_RESOURCE_SERVER_ADDR to customize hosting and image URL generation.

    Prerequisites

    Valid Google Gemini API key (GEMINI_API_KEY). Optional: BASE_URL for proxy, SERVER_LISTEN_ADDR, SERVER_PORT, and IMAGE_RESOURCE_SERVER_ADDR as environment variables.

    Details
    Last Updated1/1/2026
    SourceGitHub

    Compare Alternatives

    Similar MCP Tools

    6 related tools
    Anki MCP Server

    Anki MCP Server

    A Model Context Protocol (MCP) server that enables AI assistants to interact with Anki, the spaced repetition flashcard application. The Anki MCP Server allows AI models to access Anki's card data, enabling features like automated flashcard creation, review, and management.

    1MCP Agent

    1MCP Agent

    A unified Model Context Protocol server implementation that aggregates multiple MCP servers into one. The 1mcp-app/agent is an open-source project that provides a single entry point for multiple MCP servers, making it easier to manage and interact with various AI models and tools.

    Roundtable AI MCP Server

    Roundtable AI MCP Server

    Roundtable AI MCP Server is a zero-configuration local MCP server that unifies multiple AI coding assistants (Codex, Claude Code, Cursor, Gemini) through intelligent auto-discovery and a standardized interface. It coordinates specialized sub-agents from within your IDE to solve engineering problems in parallel, sharing context and synthesizing responses into a single, high-quality output. This documentation details installation, available MCP tools, integration with popular IDEs, and a broad ecosystem of specialized tools and CLIs that can be invoked as part of a roundtable-powered workflow, enabling developers to delegate tasks to the right AI for each facet of a problem without leaving their development environment.

    MCPJungle

    MCPJungle

    MCPJungle is a self-hosted MCP Gateway and Registry for AI agents. It serves as a central registry and gateway to manage Model Context Protocol (MCP) servers and the tools they expose. By consolidating MCP server registration, tool discovery, and access control, MCPJungle enables AI agents and clients to discover, group, and securely invoke tools from a single, unified gateway. The project provides a CLI, Docker-based deployment options, and enterprise-ready features such as tool grouping, access control, and observability to streamline MCP-based workflows across organizations.

    mcpmcp-server

    mcpmcp-server

    mcpmcp-server is a focused solution for discovering, setting up, and integrating MCP servers with your favorite clients to unlock AI-powered workflows. It streamlines how you connect MCP-powered servers to popular clients, enabling seamless AI-assisted interactions across your daily tools. The project emphasizes an approachable, config-driven approach to linking MCP servers with clients like Claude Desktop, while directing you to the homepage for variations across apps and platforms. This README highlights a practical JSON configuration example and notes on supported environments, helping you get started quickly and confidently.

    Anyquery MCP

    Anyquery MCP

    Anyquery MCP is the Model Context Protocol endpoint for the Anyquery SQL engine, enabling large language models (LLMs) like ChatGPT and Claude to connect to and query data through MCP. This MCP server acts as a bridge between LLMs and Anyquery’s data integrations, allowing LLMs to contextually access files, databases, and apps via the MCP interface. It complements Anyquery’s SQL querying capabilities by providing a standardized, secure channel for LLMs to request data access, execute SQL-like interactions, and receive structured results. The MCP integration is designed to be used in conjunction with Anyquery’s SQL runtime, including its MySQL-compatible server mode for client tooling, and is part of the broader MCP-enabled ecosystem described in the project docs.