Content
# 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**](https://docs.aws.amazon.com/cli/v1/userguide/cli-configure-files.html)
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`
<details>
<summary>Development/Unpublished Servers Configuration</summary>
```json
{
"mcpServers": {
"s3-mcp-server": {
"command": "uv",
"args": [
"--directory",
"/Users/user/generative_ai/model_context_protocol/s3-mcp-server",
"run",
"s3-mcp-server"
]
}
}
}
```
</details>
<details>
<summary>Published Servers Configuration</summary>
```json
{
"mcpServers": {
"s3-mcp-server": {
"command": "uvx",
"args": [
"s3-mcp-server"
]
}
}
}
```
</details>
## Development
### Building and Publishing
To prepare the package for distribution:
1. Sync dependencies and update lockfile:
```bash
uv sync
```
2. Build package distributions:
```bash
uv build
```
This will create source and wheel distributions in the `dist/` directory.
3. Publish to PyPI:
```bash
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](https://github.com/modelcontextprotocol/inspector).
You can launch the MCP Inspector via [`npm`](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm) with this command:
```bash
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](CONTRIBUTING.md#security-issue-notifications) for more information.
## License
This library is licensed under the MIT-0 License. See the LICENSE file.
