What is Sample Mcp Server S3

sample-mcp-server-s3 is an implementation of a Model Context Protocol (MCP) server designed to retrieve data, specifically PDF documents, from Amazon S3 storage.

Use cases

Use cases include integrating S3-stored PDF documents into applications that require context-aware data retrieval, such as AI models or data analysis tools.

How to use

To use sample-mcp-server-s3, configure your AWS credentials with appropriate permissions, set up the server configuration in your application, and run the server using the provided commands for either development or published environments.

Key features

Key features include exposing AWS S3 data through resources, supporting PDF document retrieval, and providing tools like ListBuckets, ListObjectsV2, and GetObject for interacting with S3.

Where to use

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Sample S3 Model Context Protocol Server

An MCP server implementation for retrieving data such as PDF’s from S3.

Features

Resources

Expose AWS S3 Data through Resources. (think of these sort of like GET endpoints; they are used to load information into the LLM’s context). Currently only PDF documents supported and limited to 1000 objects.

Tools

• ListBuckets
  • Returns a list of all buckets owned by the authenticated sender of the request
• ListObjectsV2
  • Returns some or all (up to 1,000) of the objects in a bucket with each request
• GetObject
  • Retrieves an object from Amazon S3. In the GetObject request, specify the full key name for the object. General purpose buckets - Both the virtual-hosted-style requests and the path-style requests are supported

Configuration

Setting up AWS Credentials

1. Obtain AWS access key ID, secret access key, and region from the AWS Management Console and configure credentials files using Default profile as shown here
2. Ensure these credentials have appropriate permission READ/WRITE permissions for S3.

Usage with Claude Desktop

Claude Desktop

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "s3-mcp-server": {
      "command": "uv",
      "args": [
        "--directory",
        "/Users/user/generative_ai/model_context_protocol/s3-mcp-server",
        "run",
        "s3-mcp-server"
      ]
    }
  }
}

{
  "mcpServers": {
    "s3-mcp-server": {
      "command": "uvx",
      "args": [
        "s3-mcp-server"
      ]
    }
  }
}

Development

Building and Publishing

To prepare the package for distribution:

1. Sync dependencies and update lockfile:

uv sync

2. Build package distributions:

uv build

This will create source and wheel distributions in the dist/ directory.

3. Publish to PyPI:

uv publish

Note: You’ll need to set PyPI credentials via environment variables or command flags:

• Token: --token or UV_PUBLISH_TOKEN
• Or username/password: --username/UV_PUBLISH_USERNAME and --password/UV_PUBLISH_PASSWORD

Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging
experience, we strongly recommend using the MCP Inspector.

You can launch the MCP Inspector via npm with this command:

npx @modelcontextprotocol/inspector uv --directory /Users/user/generative_ai/model_context_protocol/s3-mcp-server run s3-mcp-server

Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.

Security

See CONTRIBUTING for more information.

License

This library is licensed under the MIT-0 License. See the LICENSE file.