awslabs-s3-tables-mcp-server

import subprocess import os import json # NOTE: Ensure AWS credentials are configured (e.g., via `aws configure` or environment variables). # The server will pick them up from the environment. # For example: # os.environ['AWS_ACCESS_KEY_ID'] = os.environ.get('AWS_ACCESS_KEY_ID', 'YOUR_ACCESS_KEY') # os.environ['AWS_SECRET_ACCESS_KEY'] = os.environ.get('AWS_SECRET_ACCESS_KEY', 'YOUR_SECRET_KEY') # os.environ['AWS_REGION'] = os.environ.get('AWS_REGION', 'us-east-1') # 1. Define a minimal mcp_config.json content. # This configuration tells an MCP client how to start the S3 Tables MCP server. # In a real scenario, this file would be loaded by an AI client. # For demonstration, we simulate running the command directly. mcp_config_content = { "mcpServers": { "awslabs.s3-tables-mcp-server": { "command": "uvx awslabs.s3-tables-mcp-server --profile default --region us-east-1", "description": "MCP Server for Amazon S3 Tables", "schemas": ["file://~/.config/mcp/awslabs.s3-tables-mcp-server.json"] } } } # 2. To run the server directly, you would typically execute a command. # This example shows how an AI client might configure and run it. # For local testing, ensure 'uv' is installed (`pip install uv`). print("Attempting to run awslabs.s3-tables-mcp-server...") print("This server typically runs in a long-lived process and interacts with AI clients via stdio.") print("Press Ctrl+C to stop the server if it starts successfully.") # Example of how an MCP client would launch the server process try: # The actual command might vary based on your AWS profile and region. # Using --allow-write is generally not recommended for quickstart without understanding implications. command_to_run = [ "uvx", "awslabs.s3-tables-mcp-server", "--profile", os.environ.get('AWS_PROFILE', 'default'), # Use 'default' or actual profile "--region", os.environ.get('AWS_REGION', 'us-east-1'), # Use 'us-east-1' or actual region # Add --allow-write if you intend to enable write operations, but be cautious! # "--allow-write" ] # Starting the server in a non-blocking way for demonstration. # In a real setup, this would be managed by the AI client application. process = subprocess.Popen(command_to_run, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True) print("Server process started (PID: {})".format(process.pid)) print("Output (first few lines, server runs indefinitely):") # Read some output to confirm it's running, then terminate for quickstart. # In a real scenario, the process would continue running and communicate via stdio. for _ in range(5): line = process.stdout.readline() if line: print(line.strip()) else: break # For a quickstart, we often don't want a blocking server. # You would typically interact with this server via an AI client (e.g., Kiro, Cline). # For this example, we'll terminate it after a short period. print("\n--- Server started. Terminating quickstart process. ---") print("To interact, connect an AI client configured with this server.") process.terminate() process.wait(timeout=5) except FileNotFoundError: print("Error: 'uvx' command not found. Please ensure 'uv' is installed and in your PATH.") except Exception as e: print(f"An error occurred: {e}")