patchy631/ai-engineering-hub
AI Engineering Hub offers in-depth tutorials and practical resources on Large Language Models, Retrieval-Augmented Generation, and real-world AI agent applications for all skill levels.
Awesome MCP › MCP Resources & Educational Materials
aws-samples/sample-agents-with-nova-act-and-mcp
This repository is an AWS sample that demonstrates how to build web automation agents with Amazon Nova Act and connect them to language models through the Model Context Protocol. It explains the MCP client and server architecture in the terms of this example: the host is Claude Desktop or another AI tool, the client keeps a one to one connection with the server, the server is a Nova Act process that exposes web automation as standard MCP tools, and the remote services are the websites the browser drives. Foundation models are reached through Amazon Bedrock, with Claude 3.5 Sonnet V2 enabled by default. The code is organised into three directories. mcp_examples holds nova_act_mcp_server.py, the MCP compliant server exposing Nova Act capabilities, and nova_act_mcp_client.py, a sample client; running the client starts the server and connects to it. nova_act_examples holds plain Nova Act scripts, one showing basic navigation, search and extraction on a product page, and one showing parallel task execution with a thread pool, structured data extraction and error handling. streamlit_examples holds a research tool that finds top games for a chosen console, queries Amazon in parallel for prices and details, and renders the results as an interactive table that can be saved. The documented capabilities include navigation and interaction, parallel browser tasks, data extraction, screenshot capture for visual verification, and session and browser lifecycle control. Prerequisites are macOS or Ubuntu, Python 3.10 or newer, a Nova Act API key, Node.js for Claude Desktop, and configured AWS credentials with Bedrock enabled. The best practice notes advise closing browser sessions, using headless mode for unattended runs, splitting complex actions into small instructions and using schemas for structured responses. It is licensed MIT-0 and is aimed at developers learning how these technologies fit together.
https://github.com/aws-samples/sample-agents-with-nova-act-and-mcp
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