Project setup

Project Overview: CLI Chatbot Implementation

• Goal: To build a CLI-based chatbot to demonstrate the interaction between MCP clients and MCP servers.
• Architecture: The project implements both a custom MCP client and a custom MCP server within a single project environment.
• Functionality:
• The chatbot allows users to interact with a collection of documents.
• Documents are simulated and stored only in memory (no persistence).
• The MCP server includes two core tools:
• Tool 1: Read document contents.
• Tool 2: Update document contents.
• **Conceptual Understanding (Client vs. Server):**
• In a typical project, you build either an MCP client (to connect to external services) or an MCP server (to distribute a service).
• This project combines both to illustrate how they work together.

Project Setup and Execution

• Initial Steps:
• Download and extract the CLIProject. zip starter code.
• Review the README document for critical setup instructions.
• Configuration:
• Set environment variables, specifically defining the Bedrock region and a Model ID.
• Installation:
• Set up a Python environment.
• Install required dependencies.
• Running the Application:
• Navigate to the project directory in the terminal.
• Execute the main file using the appropriate command (e. g. , uv run main. py or python main. py).
• A chat prompt will appear, allowing for immediate interaction.

Takeaways

• The project implements a CLI chatbot to demonstrate the interaction between custom MCP clients and MCP servers.
• The MCP server provides core functionality through tools to Read and Update in-memory documents.
• The architecture combines both client and server roles within a single environment to illustrate their collaborative function.
• Setup requires configuring environment variables, specifically defining the Bedrock region and a Model ID.