How to encode user intent history into the MCP structure?

 

Step 1: Understand the MCP Structure

 

Before encoding user intent history into the MCP structure, familiarize yourself with the components of MCP. MCP, or Model Context Protocol, serves as a framework to deliver structured context to language models, ensuring predictable and efficient use. Its components commonly include:

• System Instructions
• User Profile
• Document Context
• Active Tasks / Goals
• Tool Access
• Rules / Constraints

 

Step 2: Collect User Intent History

 

Gather all relevant user data that reflects their intentions. This includes previous interactions, preferences, goals, and any patterns observed in past behavior. Ensure the information is well-organized and categorized for easier encoding.

 

Step 3: Define System Instructions

 

Create a clear set of instructions, which describe the model's role and functions. This serves as a guide for interacting with users and processing their intents. For instance:

System Instructions: "You are a versatile assistant expected to understand and leverage user history to improve interaction quality."

 

Step 4: Construct the User Profile

 

Incorporate user-specific details into the MCP structure. This should include their name, preferences, goals, and any pertinent historical data. This profile helps tailor the model's responses to individual needs:

User Profile: {  
  Name: "John Doe",  
  Preferences: ["Conciseness", "Visual Aids"],  
  Goals: ["Learning Data Science", "Improving Python Skills"]  
}

 

Step 5: Embed Document Context

 

If relevant, include any documents or resources that the user regularly interacts with. This could be articles, notes, previous conversation logs, or uploaded files that support ongoing tasks:

Document Context: {  
  RecentUploads: ["IntrotoDataScience.pdf", "PythonCookbook.docx"]  
}

 

Step 6: Define Active Tasks/Goals

 

Clearly outline the tasks or objectives the user is currently focused on. These should align with their overarching goals and provide context for their immediate interactions:

Active Tasks: {  
  CurrentObjectives: ["Complete Lesson 4 of Data Science Course", "Solve Python Challenge"]  
}

 

Step 7: Specify Tool Access

 

Determine which tools the model can utilize to better assist the user. This might include APIs, databases, external applications, or web browsers that facilitate various tasks:

Tool Access: ["WebSearchAPI", "PythonExec", "SQLDatabase"]

 

Step 8: Set Rules and Constraints

 

Identify any boundaries or restrictions that the model must adhere to while interacting with the user. This ensures outputs remain appropriate and within desired realms:

Rules/Constraints: {  
  Restrictions: ["Avoid Medical Advice", "Limit to Data Science Topics"]  
}

 

Step 9: Encode User Intent History into MCP

 

Integrate the user intent history with the MCP components prepared in previous steps. Ensure that each piece of historical intent is tied to specific elements within the MCP framework. For example:

MCP: {  
  SystemInstructions: "You are a versatile assistant...",  
  UserProfile: {  
    Name: "John Doe",  
    Preferences: ["Conciseness", "Visual Aids"],  
    Goals: ["Learning Data Science", "Improving Python Skills"]  
  },  
  DocumentContext: {  
    RecentUploads: ["IntrotoDataScience.pdf", "PythonCookbook.docx"]  
  },  
  ActiveTasks: {  
    CurrentObjectives: ["Complete Lesson 4 of Data Science Course", "Solve Python Challenge"]  
  },  
  ToolAccess: ["WebSearchAPI", "PythonExec", "SQLDatabase"],  
  RulesConstraints: {  
    Restrictions: ["Avoid Medical Advice", "Limit to Data Science Topics"]  
  }  
}

 

Step 10: Implement and Test

 

Apply this encoded user intent history in a practical setting with your AI/LLM model. Test to ensure that the model interprets and enhances interactions based on this structured context. Validate it with different scenarios to evaluate its effectiveness and adjustments as necessary.