Gemini MCP File Agent (MVP)

Name: Gemini MCP File Agent (MVP)
Author: kevingduck

@kevingduck

リポジトリ

Gemini MCP File Agent (MVP) について

Simple chat interface and server for testing Gemini MCP

#gemini #mcp #file-agent

基本情報

カテゴリ

AI とエージェント

ランタイム

python

トランスポート

stdio

公開者

kevingduck

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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.

FAQ from Gemini MCP File Agent (MVP)

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.

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.

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.

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/.

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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