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Gemini MCP File Agent (MVP)

@kevingduck

代码仓库

基本信息

分类

AI 与智能体

传输方式

stdio

作者

kevingduck

关于 Gemini MCP File Agent

Simple chat interface and server for testing Gemini MCP

#gemini#mcp#file-agent

Gemini MCP File Agent 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "gemini_mcp": {
      "command": "python",
      "args": [
        "mcp_server.py"
      ]
    }
  }
}

Gemini MCP File Agent 工具

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。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Gemini MCP File Agent (MVP)?

It is a local server and chat scripts that allow Google's Gemini AI to safely read, write, and list files on your computer within a specific, controlled folder (a sandbox). It is intended for developers exploring AI file access.

How to use Gemini MCP File Agent (MVP)?

Install the Python packages, set your Google AI API key as the GOOGLE_API_KEY environment variable, then run mcp_server.py (optionally with --sandbox-dir) in one terminal and chat_with_gemini_mcp.py in another. You can then ask Gemini about files in the sandbox.

Key features of Gemini MCP File Agent (MVP)

  • Safe file access limited to a sandbox folder.
  • Command-line chat interface with Gemini.
  • Can read, write, and list files.
  • Uses a local MCP server (mcp_server.py).
  • Designed for local, single‑computer use.

Use cases of Gemini MCP File Agent (MVP)

  • Ask Gemini what files are in your work folder.
  • Have Gemini create a file and write content to it.
  • Have Gemini read the contents of an existing file.
  • Explore giving an AI controlled file access.

常见问题

1

What is the sandbox?

The sandbox is a specific folder you choose (default: `./mcp_data_sandbox/`). The server can only touch files inside that folder for safety.

2

Why do I need an API key?

You need a Google AI API key from Google AI Studio to use Gemini. Set it as the environment variable `GOOGLE_API_KEY`.

3

Can Gemini access files outside the sandbox?

No. The `mcp_server.py` is designed to only access files inside the folder you pick as the sandbox.

4

How do I use a different sandbox folder?

Run `python mcp_server.py --sandbox-dir ./my_files` to use a custom folder instead of the default `./mcp_data_sandbox/`.

5

Is this safe for production use?

No. The README states this is a basic example and cautions you to be careful about which folder the server accesses.

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