sample-mcp-server-s3

# 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Development/Unpublished Servers Configuration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.