What are the runtime dependencies?
Python 3.8 or later, plus packages `aioboto3`, `mcp[cli]`, and `python-dotenv`.
@abdullahsayyad
基本信息
A tool written in complete python following the MCP(model context protocol). LLM can leverage this tool to work with aws S3 buckets
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"AWS-S3-AccessTool": {
"command": "python",
"args": [
"C:your-absolute-path-to-the-file\\main.py"
]
}
}
}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。
AWS S3 AccessTool is an MCP server utility that enables LLMs to interact with AWS S3 storage. It can list available buckets, retrieve stored objects, filter for CSV files, and read CSV content, with built-in async support via aioboto3. Developers can integrate it locally or via Docker, and it supports environment-based bucket selection.
Install dependencies: pip install aioboto3 mcp[cli] python-dotenv. Set environment variables AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_REGION, and optionally S3_BUCKETS to restrict access. Then configure your MCP client (e.g., Cursor.ai) with the command python and the absolute path to main.py.
Python 3.8 or later, plus packages `aioboto3`, `mcp[cli]`, and `python-dotenv`.
Set the `S3_BUCKETS` environment variable to a comma-separated list of bucket names (e.g., `bucket1,bucket2`). Only those buckets will be accessible.
Use the following JSON in your MCP configuration: `{ "mcpServers": { "AWS-S3-AccessTool": { "command": "python", "args": ["C:\\your-absolute-path-to-the-file\\main.py"] } } }`
It is open-source under the MIT License.
Yes, the README mentions integration via Docker, but no specific Docker instructions are provided.
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