Creating an AI Agent

Now comes the exciting part – creating an AI agent that can think, understand, and take action! This agent will be the friendly face of Cozy Café, helping customers and providing insights to the owner.

Your AI agent will:

• Understand natural language questions
• Use the MCP feedback server to collect and analyze feedback
• Provide helpful responses to customers
• Generate insights for the café owner

An AI agent combines intelligence (LLM) with capabilities (MCP servers) to accomplish tasks. Think of it as a smart employee who can understand instructions and use various tools.

Key Agent Capabilities

1. Natural Language Understanding - Interprets what users really want
2. Context Awareness - Remembers conversation history
3. Tool Usage - Knows when and how to use MCP servers
4. Response Generation - Creates helpful, friendly responses

Let's design our café assistant with specific personality and capabilities:

Agent Behaviors

For Customers:

• Warm greeting and friendly conversation
• Easy feedback collection
• Answer questions about menu, hours, etc.
• Thank customers genuinely

For Café Owner:

• Summarize feedback trends
• Alert about issues
• Suggest improvements
• Provide business insights

Let's build our AI agent step by step:

Now let's connect our agent to the feedback MCP server:

Now let's add real intelligence using a language model. For this example, we'll create a simple LLM interface:

Let's create a complete conversational interface:

Putting It All Together

Create a main script to run your complete AI system:

<Quiz questions={[ { question: "What is the main role of an AI agent in our system?", options: [ "To store data in files", "To understand user intent and coordinate actions using available tools", "To replace the MCP server", "To create websites" ], correctAnswer: 1, explanation: "AI agents understand what users want (using LLM intelligence) and coordinate actions by calling appropriate tools through MCP servers." }, { question: "How does the agent connect to MCP servers?", options: [ "Through direct database connections", "Using the MCP Client with stdio communication", "Via HTTP REST APIs only", "Through file system access" ], correctAnswer: 1, explanation: "The agent uses an MCP Client that communicates with servers through stdio (standard input/output), following the Model Context Protocol." }, { question: "What makes our agent 'intelligent'?", options: [ "It has a large database", "It uses an LLM to understand context and generate appropriate responses", "It runs very fast", "It has many if-else statements" ], correctAnswer: 1, explanation: "The agent's intelligence comes from using an LLM (Large Language Model) to understand user intent, maintain context, and generate natural responses." }, { question: "What is the purpose of the conversation manager?", options: [ "To save conversations to disk", "To limit conversation length", "To handle the interactive flow and coordinate between user, agent, and tools", "To translate languages" ], correctAnswer: 2, explanation: "The conversation manager handles the interactive flow, maintaining the conversation loop, and coordinating between user input, agent processing, and tool execution." } ]} />