How to combine output from multiple agents into a unified MCP?

 

Step 1: Understand the Components of MCP

 

Before combining outputs from multiple agents into a unified Model Context Protocol (MCP), you need to understand the common components that make up an MCP:

• System Instructions: These are directives like “You are a helpful assistant specialized in finance.”
• User Profile: It includes details such as the user's name, preferences, and goals.
• Document Context: It encompasses knowledge bases and recent document uploads.
• Active Tasks/Goals: These consist of current objectives and to-do lists.
• Tool Access: Specifies what tools the model can call, such as web, Python, or databases.
• Rules/Constraints: Includes limitations, such as never suggesting medical diagnoses.

 

Step 2: Gather Outputs from Multiple Agents

 

Collect the outputs from the different agents that you wish to include in the MCP. Ensure that these outputs align with the components identified in Step 1.

• Example Outputs: Gather agent-specific outputs such as chat responses, recommendations, or data entries. Ensure they map to one or more MCP components.

 

Step 3: Structure the Combined Context

 

Create a structure to combine the gathered outputs into a cohesive MCP format. This involves organizing outputs according to the MCP components. You can use pseudo-code for illustration:

mcp = {
  "system_instructions": "You are a multi-domain expert.",
  "user_profile": {
    "name": "Alice",
    "preferences": ["finance", "tech"],
    "goals": ["invest wisely", "learn about AI"]
  },
  "document_context": {
    "knowledge_base": "Financial Reports, AI Whitepapers",
    "recent_uploads": "Annual Summary.pdf"
  },
  "active_goals": ["Complete investment analysis", "Review tech trends"],
  "toolaccess": ["web", "pythontoolkit"],
  "rules": ["Avoid medical advice", "Do not exceed budget constraints"]
}

 

Step 4: Implement the MCP in an Application

 

Integrate the structured MCP into your application or workflow to manage multiple agents. This will standardize interactions and expectations across the system.

• Implementation Code: Use the following example for integrating MCP in a Python-based system.

class MCPIntegration:
    def init(self, mcp):
        self.mcp = mcp
    
    def apply_context(self):
        # Example of applying system instructions
        print("Applying Instructions:", self.mcp["system_instructions"])
        
        # Example of accessing user profile
        userprofile = self.mcp["userprofile"]
        print(f"User {userprofile['name']} with preferences {userprofile['preferences']} is being supported.")
        
        # Application of document context and goals
        self.accesstools(self.mcp["toolaccess"])
    
    def access_tools(self, tools):
        for tool in tools:
            print(f"Access to {tool} is enabled.")

 

Step 5: Test the Combined MCP

 

Once the MCP is set up in your application, test its functionality to ensure each component behaves predictably with the combined outputs.

• Testing Considerations: Simulate various scenarios using the assembled MCP and check for task completion, response accuracy, and adherence to constraints.

 

Step 6: Iterate and Optimize

 

Continuously refine the MCP based on test results and feedback. Adjust components, rules, or context as needed to enhance performance and predictability.

• Feedback Loop: Regularly collect user feedback and performance metrics to identify areas for improvement and make necessary adjustments.