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:
C:\bin\imagen3-mcp.exeC:\bin\imagen3-mcp.exeGEMINI_API_KEY 中填写你的 Gemini API 密钥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）。SERVER_LISTEN_ADDR：设置服务器监听的 IP 地址（默认为 127.0.0.1）。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）。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
Sponsored
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.
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.
Compare Alternatives
Similar MCP Tools
9 related toolsSkyvern MCP
Skyvern is the complete browser MCP for AI agents. 75+ tools for clicking, filling forms, extracting structured data, logging in with 2FA, uploading files, drag-and-drop, running JavaScript, inspecting network traffic, multi-tab browsing, and building reusable cached workflows. First workflow run uses AI; subsequent runs replay a cached script with zero LLM calls.
pageguard-mcp
pageguard-mcp is an MCP (Model Context Protocol) server that exposes PageGuard privacy compliance scanning as a set of tools for AI coding assistants. It enables seamless integration with Claude Code, Cursor, Windsurf, ChatGPT, and any MCP-compatible environment. The server supports local scans, live URL scans, and AI-generated privacy-related documents, helping developers identify tracking technologies, cookies, and third-party data processing, while also producing tailored privacy policies and compliance materials. With its straightforward MCP configuration and free local scanning capability, pageguard-mcp is designed to empower teams to maintain privacy compliance across their projects and websites.
Prop Firm Deal Finder
Prop Firm Deal Finder (PFDF) is a free MCP server that gives AI assistants real-time access to live discount codes across 20+ proprietary trading firms. It provides 6 tools: get_deals (all current discounts), search_firms (search by name/asset class), compare_firms (side-by-side comparison of challenges), find_cheapest (cheapest challenge by account size with PFDF code applied), get_firm_details (15+ data points per firm), and get_discount_code (universal PFDF code). Covers firms like Topstep, Apex Trader Funding, MyFundedFutures, FTMO, The Funded Trader, and 14 more. No API key required. Install via npx propfirmdealfinder-mcp-server or connect remotely at https://web-production-6607c.up.railway.app/mcp
mcp-server-with-spring-ai
mcp-server-with-spring-ai is a Spring Boot integrated MCP (Model Context Protocol) server example that showcases how to expose executable tools from an MCP server to clients (including LLMs) and how to wire a MCP client to consume those tools. The documentation explains MCP at a high level, outlines the three-layer MCP Java SDK architecture (Client/Server Layer, Session Layer, Transport Layer), and demonstrates two sample tools implemented in SellerAccountTools. This repo emphasizes how an MCP server can connect to external data sources (e.g., a PostgreSQL DB) and expose tools that an AI model can invoke to retrieve data, with the example illustrating tool invocation and automatic tool selection by prompts.
OpenClaw MCP Server
OpenClaw MCP Server is a secure Model Context Protocol (MCP) bridge that connects Claude.ai with a self-hosted OpenClaw assistant, enabling OAuth2 authentication and safe, controlled communication between the Claude AI ecosystem and your local or hosted OpenClaw deployment. This MCP server acts as an orchestration layer that exposes MCP tools to Claude.ai, manages authentication, and enforces security boundaries like CORS and transport options. It is designed to be deployed via Docker or run locally, with detailed installation, configuration, and security guidance provided in the documentation. By serving as a bridge, it enables Claude.ai to delegate tasks to your OpenClaw bot while preserving control over data flow and access controls, in line with MCP specifications and best security practices.
Graphiti MCP Server
Graphiti MCP Server is an experimental implementation that exposes Graphiti's real-time, temporally-aware knowledge graph capabilities through the MCP (Model Context Protocol) interface. It enables AI agents and MCP clients to interact with Graphiti's knowledge graph for structured extraction, reasoning, and memory across conversations, documents, and enterprise data. The server supports multiple backends (FalkorDB by default and Neo4j), a variety of LLM providers (OpenAI, Anthropic, Gemini, Groq, Azure OpenAI), and multiple embedder options, all accessible via an HTTP MCP endpoint at /mcp/ for broad client compatibility. It also includes queue-based asynchronous episode processing, rich entity types for structured data, and flexible configuration through config.yaml, environment variables, or CLI arguments.
Context7 MCP Server
Context7 MCP Server delivers up-to-date, code-first documentation and examples for LLMs and AI code editors by pulling content directly from the source. It supports multiple MCP clients and exposes tools that help you resolve library IDs and retrieve library documentation, ensuring prompts use current APIs and usage patterns. The repository provides installation and integration guides for Cursor, Claude Code, Opencode, and other clients, along with practical configuration samples and OAuth options for remote HTTP connections. This MCP server is designed to keep prompts in sync with the latest library docs, reducing hallucinations and outdated code snippets.
TrendRadar MCP
TrendRadar MCP is an AI-driven Model Context Protocol (MCP) based analysis server that exposes a suite of specialized tools for cross-platform news analysis, trend tracking, and intelligent push notifications. It integrates with TrendRadar’s multi-platform data aggregation (RSS and trending topics) and provides advanced AI-powered insights, sentiment analysis, and cross-platform correlation. The MCP server enables developers to query, analyze, and compare news across platforms using a consistent toolset, with ongoing updates that expand capabilities such as RSS querying, date parsing, and multi-date trend analysis. This documentation references the MCP module updates, tool additions, and architecture changes that enhance extensibility, cross-platform data handling, and AI-assisted reporting.
ChainAware Behavioural Prediction MCP
The ChainAware Behavioural Prediction MCP is an MCP-based server that provides AI-powered tools to analyze wallet behaviour prediction, fraud detection, and rug pull prediction. Designed for Web3 security and DeFi analytics, it enables developers and platforms to integrate risk assessment, predictive wallet behavior insights, and rug-pull detection through MCP-compatible clients. The server exposes three specialized tools and uses Server-Sent Events (SSE) for real-time responses, helping safeguard DeFi users, monitor liquidity risks, and score wallet or contract trustworthiness. Access to production endpoints is API-key gated, reflecting a private backend architecture that supports secure, scalable risk analytics across wallets, contracts, and pools.