What runtime and dependencies are required?
Node.js 16 or higher, TypeScript, and a valid Gemini API key from Google AI Studio.
@PV-Bhat
基本信息
Professional Gemini API integration for Claude and all MCP-compatible hosts with intelligent model selection and advanced file handling | Smithery.ai verified
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"gemsuite-mcp": {
"command": "npx",
"args": [
"-y",
"@smithery/cli@latest",
"install",
"@PV-Bhat/gemsuite-mcp",
"--client",
"claude"
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。
GemSuite MCP is an open-source server that integrates Google Gemini API capabilities with any MCP-compatible host (Claude, Cursor, Replit, etc.). It automatically selects the optimal Gemini model based on task type, content type, and complexity to deliver intelligent performance, minimize token costs, and provide seamless file handling.
Install via Smithery CLI or manually by cloning the repository, installing dependencies, and setting a Gemini API key in a .env file. After building and starting the server, the tools gem_search, gem_reason, gem_process, and gem_analyze become available directly in the assistant toolkit of any MCP-compatible host.
Node.js 16 or higher, TypeScript, and a valid Gemini API key from Google AI Studio.
It offers intelligent model selection, unified file handling across all tools, a comprehensive four-tool suite, and production-ready deployment on Smithery.ai, MCP.so, and Glama.io.
It uses Gemini 2.0 Flash (search, multimodal), 2.0 Flash-Lite (cost-efficient text processing), and 2.0 Flash Thinking (complex reasoning). Selection is automatic based on task, content, and complexity, with optional manual override via the `model_id` parameter.
Yes, you can force a specific model by passing the `model_id` parameter (e.g., `models/gemini-2.0-flash-thinking`) to any tool.
Yes, you must obtain a Gemini API key from Google AI Studio and set it as an environment variable or in a `.env` file.
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