How to build a context prioritization engine for MCP packing?

 

Step 1: Understand the Structure of MCP

 

To build a context prioritization engine for MCP packing, it's crucial first to understand the structure of the Model Context Protocol (MCP). MCP comprises several components that define:

• System Instructions: General directives for model behavior.
• User Profile: Details such as name, preferences, and goals.
• Document Context: Access to knowledge, recent documents, and uploads.
• Active Tasks / Goals: Current objectives and tasks the model should focus on.
• Tool Access: The tools and APIs available to the model.
• Rules / Constraints: Guidelines that restrict model outputs.

You need to structure these components in a standardized way to dictate how an LLM should act.

 

Step 2: Design the MCP Schema

 

Create a schema for MCP to define how context data should be organized and parsed. This might look like a JSON structure:

{
  "system_instructions": "You are a helpful assistant specialized in finance.",
  "user_profile": {
    "name": "John",
    "preferences": {
      "language": "English",
      "formality": "Casual"
    },
    "goals": ["Save money", "Understand stocks"]
  },
  "document_context": ["User uploaded financial report 2023", "Recent market analysis"],
  "active_tasks": ["Advise on stock portfolio", "Create budget plan"],
  "tool_access": ["web", "Python", "database"],
  "rules_constraints": ["Do not provide legal advice", "Avoid medical suggestions"]
}

This schema provides a blueprint for arranging and transmitting context data to the LLM.

 

Step 3: Build the Context Prioritization Logic

 

Develop an algorithm for prioritizing which pieces of context should be active and relevant at any given time. Consider criteria such as:

• Recency: Prioritize context recently referenced or updated.
• Relevance: How essential the context is to the ongoing task or user.
• Constraints: Ensure that rules and constraints are always respected.

You could use a simple prioritization algorithm, such as:

def prioritize_context(contexts):
    # Sort contexts based on custom criteria
    prioritized_contexts = sorted(contexts, key=lambda ctx: (ctx.get('recency', 0), ctx.get('relevance', 0)), reverse=True)
    return prioritized_contexts

This function can be adjusted to suit specific prioritization needs by including more sophisticated logic as necessary.

 

Step 4: Implement MCP Packing

 

Incorporate the MCP packing process into your system. This involves taking prioritized context and generating a structured packet that is passed to the LLM. You might use functions to serialize the prioritized context into JSON format:

def packmcp(prioritizedcontexts):
    mcp_packet = {
        "system_instructions": "You are a helpful assistant specialized in finance.",
        "userprofile": prioritizedcontexts.get('user_profile', {}),
        "documentcontext": prioritizedcontexts.get('document_context', []),
        "activetasks": prioritizedcontexts.get('active_tasks', []),
        "toolaccess": prioritizedcontexts.get('tool_access', []),
        "rulesconstraints": prioritizedcontexts.get('rules_constraints', []),
    }
    return json.dumps(mcp_packet)

This code efficiently packages the MCP data into the desired format for transmission to the model.

 

Step 5: Test the System

 

Once the context prioritization engine is built, it's important to thoroughly test its effectiveness. Simulate various user scenarios and evaluate whether the context prioritization correctly identifies relevant components and if the LLM responds as expected.

• Simulation: Run user interactions to verify the prioritization accuracy.
• Evaluation: Check that the LLM receives and processes the MCP data properly.
• Feedback Loop: Use test results to iteratively refine the prioritization logic.

Through comprehensive testing, you can ensure the MCP packing efficiently guides the LLM behavior.