@ENGRZULQARNAIN
Basic information
This repository provides an implementation of a Model Context Protocol (MCP) server for AWS S3, enabling AI models, particularly Large Language Models (LLMs), to securely interact with S3 buckets. The server offers a standardized interface to list S3 buckets, list objects within
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"mcp_server_s3_download_files": {
"command": "uv",
"args": [
"sync"
]
}
}
}Tool list not available yet
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We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Mcp_server_s3_download_files is an implementation of the Model Context Protocol (MCP) server for AWS S3. It provides a standardized interface enabling AI models, particularly large language models (LLMs), to securely list S3 buckets, list objects within buckets, and download file contents. The server is designed for developers integrating AI applications with AWS S3 for data retrieval and management.
Install the server via PyPI using uv add mcp-s3-server (recommended) or pip install mcp-s3-server. For a development install, clone the repository and run uv sync or pip install -e .. Configure AWS credentials using the credentials file, environment variables, or IAM roles. Start the server by running the mcp-s3-server command, then integrate with an AI model via the MCP interface.
You need Python 3.10 or higher, configured AWS credentials (Access Key ID, Secret Access Key, and Region), the `uv` package manager (recommended) or pip, and familiarity with the Model Context Protocol.
The recommended method is to install from PyPI using `uv add mcp-s3-server` or `pip install mcp-s3-server`. For development, clone the repository and run `uv sync` or `pip install -e .` in the project directory.
You can configure credentials using the AWS credentials file (`~/.aws/credentials`), environment variables (`AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, `AWS_DEFAULT_REGION`), or IAM roles (for EC2 instances).
Some versions may support only specific file types (e.g., PDFs), have limits on the number of retrieved objects (e.g., up to 1000 objects), or require configuration for the maximum number of buckets returned.
After configuring AWS credentials, run the command `mcp-s3-server` in your terminal. The server will start and be ready to integrate with an AI model via the MCP interface.
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