How to automatically summarize memory into persistent MCP blocks?

 

Step 1: Understand the Model Context Protocol (MCP)

 

• MCP Definition: MCP is a standardized protocol to define context for language models (LLMs) to enhance their predictability and effectiveness. It includes:
• Long-term memory
• Instructions and goals
• Active context
• Guardrails or constraints

 

Step 2: Identify Components of MCP

 

• Common Components of MCP:
• System Instructions: For example, "You are a helpful assistant specialized in finance."
• User Profile: Includes user's name, preferences, and goals.
• Document Context: Refers to the knowledge base and recent uploads.
• Active Tasks/Goals: Current objectives and to-dos the model should adhere to.
• Tool Access: Specifies what tools the model can call, such as web access or databases.
• Rules/Constraints: E.g., "never suggest medical diagnoses."

 

Step 3: Plan the Summary Automation

 

• Define the Objective: Automate the process of summarizing memory into MCP blocks.
• Determine Required Information: Needs to include system instructions, user profile, document context, etc.

 

Step 4: Implement the MCP Framework

 

class MCPBlock:
    def init(self, systeminstructions, userprofile, documentcontext, activetasks, toolaccess, rulesconstraints):
        self.systeminstructions = systeminstructions
        self.userprofile = userprofile
        self.documentcontext = documentcontext
        self.activetasks = activetasks
        self.toolaccess = toolaccess
        self.rulesconstraints = rulesconstraints
    
    def summarize(self):
        return {
            "System Instructions": self.system_instructions,
            "User Profile": self.user_profile,
            "Document Context": self.document_context,
            "Active Tasks/Goals": self.active_tasks,
            "Tool Access": self.tool_access,
            "Rules/Constraints": self.rules_constraints
        }

 

Step 5: Create Function to Automatically Summarize Memory

 

def summarizetomcp(memory):
    systeminstructions = extractsystem_instructions(memory)
    userprofile = extractuser_profile(memory)
    documentcontext = extractdocument_context(memory)
    activetasks = extractactive_tasks(memory)
    toolaccess = extracttool_access(memory)
    rulesconstraints = extractrules_constraints(memory)
    
    mcpblock = MCPBlock(systeminstructions, userprofile, documentcontext, activetasks, toolaccess, rules_constraints)
    return mcp_block.summarize()

 

Step 6: Implement Functions to Extract Information

 

def extractsysteminstructions(memory):
    # Implement logic to extract system instructions from memory
    pass

def extractuserprofile(memory):
    # Implement logic to extract user profile from memory
    pass

def extractdocumentcontext(memory):
    # Implement logic to extract document context from memory
    pass

def extractactivetasks(memory):
    # Implement logic to extract active tasks from memory
    pass

def extracttoolaccess(memory):
    # Implement logic to extract tool access information from memory
    pass

def extractrulesconstraints(memory):
    # Implement logic to extract rules and constraints from memory
    pass

 

Step 7: Test the Automation

 

• Prepare a Sample Memory: Simulate LLM interactions and store information.
• Run the summarization function: Verify if the function outputs a correctly structured MCP block.

 

Step 8: Iterate and Improve

 

• Enhance Extraction Logic: Refine each function to improve accuracy.
• Validation: Ensure that each component is correctly identified and structured in the MCP block.

 

Step 9: Deploy and Monitor

 

• Deploy: Use in environment where an LLM requires structured context.
• Monitor: Gather feedback on performance and make necessary adjustments to improve effectiveness and accuracy.