How to implement context scoping (global vs local vs ephemeral)?

 

Step 1: Understand MCP and Its Purpose

 

• Define MCP:

  MCP is the Model Context Protocol, a standard for structuring context in LLMs.

• Purpose:

  MCP aims to make model behavior predictable, enables modular memory, and allows for fine-grained control over AI interactions.

 

Step 2: Identify the Key Components of MCP

 

• System Instructions:

  Define what the model is supposed to do (e.g., “You are a helpful assistant specialized in finance.”).

• User Profile:

  Include information such as name, preferences, and goals.

• Document Context:

  Provide a knowledge base or recent uploads as context for the model.

• Active Tasks/Goals:

  Outline the current objectives or to-dos for the AI.

• Tool Access:

  Specify what the model can call or access, like web or databases.

• Rules/Constraints:

  Set guardrails such as avoiding specific outputs or staying within a domain.

 

Step 3: Implement Global Context

 

• Global Context:

  This refers to information or settings that are constantly accessible to the model.

  
  global_context = {
    "system_instructions": "You are a helpful assistant focused on finance.",
    "tool_access": ["web", "database"]
  }
  

• Make sure these parameters are loaded at the initialization phase of your LLM.

 

Step 4: Implement Local Context

 

• Local Context:

  This is context specific to a session or conversation. It usually resets after the session ends.

  
  def getlocalcontext(userprofile, conversationhistory):
    return {
        "userprofile": userprofile,
        "conversationhistory": conversationhistory
    }

localcontext = getlocalcontext(userprofile, conversation_history)


• This context should be refreshed or cleared when a new session starts to maintain accuracy.

 

Step 5: Implement Ephemeral Context

 

• Ephemeral Context:

  This context is transient and relevant only during the execution of a specific task or operation.

  
  ephemeral_context = {
    "active_tasks": ["analyze quarterly report"],
    "temporary_data": ["user input during the task"]
  }
  

• Ephemeral context should be discarded after the task is complete.

 

Step 6: Apply MCP in an LLM System

 

• Integrate your contexts within the LLM's operation pipeline.

  
  def processrequest(globalcontext, localcontext, ephemeralcontext):
    mergedcontext = {globalcontext, **localcontext, ephemeralcontext}
    # Pass the merged context to the LLM
    response = llm.processcontext(mergedcontext)
    return response
  

• Ensure that the assembled context is communicated clearly for predictable AI behavior.

 

Step 7: Test MCP Implementation

 

• Conduct various test cases to confirm that contexts are being correctly interpreted by the model and achieving the desired predictable behavior.
• Adjust and refine context components as needed for different scenarios or system demands.

 

Step 8: Document and Maintain MCP Systems

 

• Maintain documentation that clearly outlines how contexts are structured and utilized in your system.
• Regular updates and refinements should be documented for clarity and compliance with any new MCP standards that may emerge.

 

By following these steps, you will implement a comprehensive MCP system that structures contexts effectively in AI systems, enhancing predictability and flexibility.