Building Advanced MCP (Model Context Protocol) Agents with Multi-Agent Coordination, Context Awareness, and Gemini Integration

Building Advanced MCP (Model Context Protocol) Agents with Multi-Agent Coordination, Context Awareness, and Gemini Integration

In this tutorial, we will guide you through the process of building an advanced MCP (Model Context Protocol) Agent that is designed to operate smoothly in Jupyter or Google Colab environments. The focus is on real-world applicability, emphasizing multi-agent coordination, context awareness, memory management, and dynamic tool usage.

As we proceed, we will observe how each agent takes on specific roles—ranging from coordinating tasks, conducting research, analyzing data, to executing actions. Together, they form a cohesive swarm capable of managing complex tasks efficiently.

Understanding the Target Audience

The primary audience comprises tech-savvy business managers, AI researchers, and developers interested in enhancing their strategic decision-making through advanced AI systems. This audience typically experiences the following pain points:

• Challenges in integrating AI solutions within existing business frameworks
• The need for clear guidelines in developing and managing multi-agent systems
• Desires for specific use cases demonstrating the real-world application of AI-driven agents

Their goals include:

• Maximizing the efficiency of AI implementations
• Improving productivity through automated processes
• Enhancing data-driven decision-making with sophisticated analytical tools

Interests typically lie in efficient data management, the latest trends in AI architectures, and practical implementations that can be translated into business value. When it comes to communication preferences, this audience tends to favor concise, clear instructions, supported by documented tutorials and code snippets.

Setting Up an MCP Agent

We will begin by integrating essential Python libraries for data handling, logging, and agent structuring. Following this, we will configure logging for better debugging and check the availability of the Gemini API for seamless integration.

Here is an example of setting up the agent environment:

import json
import logging
from typing import Dict, List, Any, Optional
from dataclasses import dataclass
from enum import Enum
from datetime import datetime
import time

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

try:
    import google.generativeai as genai
    GEMINI_AVAILABLE = True
except ImportError:
    print("  google-generativeai not installed. Run: pip install google-generativeai")
    GEMINI_AVAILABLE = False

Essential libraries are imported, and logging is set up to facilitate efficient debugging. The code also checks for the installation of the Gemini API, enabling its use if available, or falling back to a demo mode if not.

Defining Agent Roles and Context

Next, we define the fundamental building blocks of our agent system:

class AgentRole(Enum):
    COORDINATOR = "coordinator"
    RESEARCHER = "researcher" 
    ANALYZER = "analyzer"
    EXECUTOR = "executor"

@dataclass
class Message:
    role: str
    content: str
    timestamp: datetime
    metadata: Dict[str, Any] = None

@dataclass
class AgentContext:
    agent_id: str
    role: AgentRole
    capabilities: List[str]
    memory: List[Message]
    tools: List[str]

We establish an enumerated type for AgentRole, allowing us to assign clear responsibilities. The Message class facilitates the storage of conversational context, while the AgentContext retains each agent’s identity, role, memory, and available tools, which aids in effective interaction management.

Creating an MCP Agent

The MCPAgent class embodies the advanced capabilities of our agents, ensuring that it is compatible with Jupyter environments. Here’s a simplified view of its initialization process:

class MCPAgent:
    def __init__(self, agent_id: str, role: AgentRole, api_key: str = None):
        self.agent_id = agent_id
        self.role = role
        self.api_key = api_key
        self.memory = []
        self.context = AgentContext(
            agent_id=agent_id,
            role=role,
            capabilities=self._init_capabilities(),
            memory=[],
            tools=self._init_tools()
        )
        
        self.model = None
        if GEMINI_AVAILABLE and api_key:
            try:
                genai.configure(api_key=api_key)
                self.model = genai.GenerativeModel('gemini-pro')
                print(f" Agent {agent_id} initialized with Gemini API")
            except Exception as e:
                print(f"  Gemini configuration failed: {e}")
                print(" Running in demo mode with simulated responses")
        else:
            print(f" Agent {agent_id} running in demo mode")

In this setup, each agent initializes its role-specific capabilities and tools based on its assigned role, maintains a memory of messages, and generates context-aware responses. Integration with Gemini is seamless when available, with a fallback to demo responses otherwise.

Efficient Task Coordination

The MCPAgentSwarm class manages a group of specialized agents, allowing for smooth coordination of tasks:

class MCPAgentSwarm:
    def __init__(self, api_key: str = None):
        self.api_key = api_key
        self.agents = {}
        self.task_history = []
        self.results = {}

    def coordinate_task(self, task: str) -> Dict[str, Any]:
        if "coordinator" not in self.agents:
            self.create_agent("coordinator", AgentRole.COORDINATOR)
        
        coordinator = self.agents["coordinator"]
        decomposition = coordinator.process_message(
            f"Decompose this complex task into subtasks and identify which specialized agents are needed: {task}"
        )
        # Further coordination logic here

This class not only creates agents as required but also coordinates complex tasks through decomposition, collaboration, and final synthesis. It tracks results and historical data, ensuring all agents necessary for task completion are available.

Running the Demo

Finally, we provide a demonstration of the advanced MCP agent capabilities. The demo includes single-agent interaction, multi-agent collaboration, and monitoring of the swarm’s status:

def demo_notebook_compatible():
    swarm = MCPAgentSwarm(API_KEY)
    researcher = swarm.create_agent("research_agent", AgentRole.RESEARCHER)
    result = researcher.process_message("Research the latest trends in AI agent architectures and multi-agent systems")
    # Display results  

This process illustrates how the agents can interact, coordinate, and synthesize outputs effectively. By visualizing these interactions, users can grasp the power of the MCP agents in a practical, grounded manner.

Conclusion

This tutorial underscores the effectiveness of MCP agents in coordinating, decomposing tasks, and synthesizing results into actionable insights within a notebook-friendly design. With Gemini integration offering real AI responses and a fallback for demo modes, this foundation for advanced multi-agent systems is poised for practical application across various fields.

For further exploration, refer to our GitHub Page for additional tutorials, codes, and notebooks.

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