The Building Blocks - Understanding MCP, Agents, and LLMs (Part 2/5)

In Part 1, we learned that AI systems are like smart orchestra conductors. Now, let's meet the musicians in our orchestra and understand how they work together. By the end of this article, you'll understand the core components we'll use to build our AI system.

The Four Essential Components

1. Workflow Engines: The Conductor

A workflow engine orchestrates the entire process. Think of it as a recipe that says:

1. First, collect the customer feedback
2. Then, analyze the sentiment
3. Next, categorize the issues
4. Finally, generate a summary report

Popular workflow engines include:

• n8n: Visual workflow automation
• Temporal: Code-based workflows
• Apache Airflow: Data pipeline orchestration

2. AI Agents: The Specialized Workers

AI agents are like skilled employees, each with a specific job:

• Research Agent: Gathers information from various sources
• Analysis Agent: Processes and understands data
• Writing Agent: Creates reports and summaries
• Action Agent: Performs tasks like sending emails

Here's a simple conceptual example:

class FeedbackAnalysisAgent:
    def __init__(self, llm):
        self.llm = llm
        self.name = "Feedback Analyzer"

    def analyze(self, feedback_text):
        prompt = f"Analyze this feedback and identify key themes: {feedback_text}"
        return self.llm.complete(prompt)

3. Large Language Models: The Brains

LLMs are the intelligence behind AI agents. They:

• Understand natural language
• Generate human-like responses
• Analyze patterns and sentiment
• Make contextual decisions

Popular options:

• OpenAI GPT-4: Powerful and versatile
• Anthropic Claude: Great for analysis and coding
• Local models: Privacy-focused alternatives

4. MCP Servers: The Universal Connectors

Here's where things get exciting. Model Context Protocol (MCP) is like a universal adapter that lets AI systems connect to any tool or service.

Imagine you have:

• A filing cabinet (your database)
• A telephone (communication system)
• A calculator (processing tool)

MCP creates a standard way for AI to use all these tools, regardless of their original design.

How MCP Works: A Simple Analogy

Think of MCP like a restaurant menu:

1. Resources = Menu Sections

   • "Today's Specials" (dynamic data)
   • "Regular Menu" (static information)

2. Tools = Things You Can Order

   • "Get me the soup of the day"
   • "I'll have the daily special"
   • "Can you modify this dish?"

3. Server = The Waiter

   • Takes your requests
   • Communicates with the kitchen
   • Brings back results

Why MCP Changes Everything

Before MCP, connecting AI to different tools was like needing a different translator for each country you visit. With MCP, it's like everyone suddenly speaks the same language.

Benefits:

• Standardization: One protocol for all integrations
• Flexibility: Easy to add new capabilities
• Security: Clear boundaries and permissions
• Simplicity: Less code, more functionality

A Sneak Peek: Our MCP Server Structure

Here's what we'll build in the upcoming parts:

from mcp.server import Server, Resource, Tool

class CustomerFeedbackServer:
    def __init__(self):
        self.server = Server("feedback-analyzer")
        self.feedback_store = []

    def setup(self):
        # Define what data we expose
        self.server.add_resource(Resource(
            uri="feedback://recent",
            name="Recent Feedback",
            description="Last 10 customer feedback entries"
        ))

        # Define what actions we can take
        self.server.add_tool(Tool(
            name="analyze_sentiment",
            description="Analyze emotional tone of feedback",
            input_schema={
                "type": "object",
                "properties": {
                    "text": {"type": "string"}
                }
            }
        ))

Putting It All Together

When these components work together:

1. Workflow Engine says: "Time to analyze today's feedback"
2. AI Agent receives the task and thinks: "I need to get the feedback first"
3. MCP Server provides access to the feedback data
4. LLM analyzes the sentiment and extracts insights
5. Results flow back through the system to generate reports

What's Next?

In Part 3, we'll roll up our sleeves and set up our development environment. You'll:

• Install Python and the MCP SDK
• Set up your first MCP server
• Test basic connectivity
• Prepare for building our feedback analysis system

The exciting part? Once you understand these building blocks, you can apply them to ANY business problem.

What tools or services would you want to connect to an AI system? Drop a comment below - your idea might become our next example!