How to normalize context input before injecting into MCP?

 

Step 1: Understand the Importance of Context Normalization

 

Context normalization is about converting varied, unstructured context data into a standard format before feeding it into the Model Context Protocol (MCP). By doing this, you make sure that information is consistently understood by the language model, leading to more predictable and controllable outputs. The main areas to focus on include long-term memory, active tasks, document context, user profiles, and constraints.

 

Step 2: Define System Instructions

 

System instructions set the overall behavior of the model. Craft a concise instruction that clearly defines the model's role and domain. This acts as a guide for how the model should interpret other context inputs.

"You are a helpful assistant specialized in finance."

 

Step 3: Normalize User Profile Information

 

Ensure user profiles contain structured information like name, preferences, and goals. Use a consistent format for easy parsing.

{
    "name": "Alex",
    "preferences": ["short responses", "data-driven"],
    "goals": ["learn about investing", "save for retirement"]
}

 

Step 4: Structure Document Context

 

Organize document-related information coherently, such as a knowledge base or recent uploads. Make the data easily accessible, ideally in a structured format like JSON.

{
    "knowledge_base": ["Document1.pdf", "Financial Report Q2 2023"],
    "recent_uploads": ["BudgetAnalysis.xlsx"]
}

 

Step 5: Clarify Active Tasks and Goals

 

List current tasks and objectives straightforwardly. This clarity allows the LLM to prioritize actions accordingly. Use a structured task list with detailed subtasks if necessary.

{
    "active_tasks": [
        "Analyze stock portfolio",
        "Draft monthly financial report"
    ]
}

 

Step 6: Define Tool Access

 

Specify which external tools or APIs the model can use. This capability enhances the model's functionality by allowing it to pull in or push out data as required.

{
    "tool_access": ["web", "Python", "database"]
}

 

Step 7: Implement Rules and Constraints

 

Rules and constraints serve to guide output and ensure safety and relevance. Structure these directives to maintain a balance between openness and restriction.

{
    "rules": [
        "Never suggest medical diagnoses",
        "Stay within the domain of finance"
    ]
}

 

Step 8: Aggregate Context Components

 

After normalizing individual components, combine them into a cohesive context input for the MCP. This holistic view helps the LLM understand its operating parameters.

{
    "system_instructions": "You are a helpful assistant specialized in finance.",
    "user_profile": {
        "name": "Alex",
        "preferences": ["short responses", "data-driven"],
        "goals": ["learn about investing", "save for retirement"]
    },
    "document_context": {
        "knowledge_base": ["Document1.pdf", "Financial Report Q2 2023"],
        "recent_uploads": ["BudgetAnalysis.xlsx"]
    },
    "active_tasks": [
        "Analyze stock portfolio",
        "Draft monthly financial report"
    ],
    "tool_access": ["web", "Python", "database"],
    "rules": [
        "Never suggest medical diagnoses",
        "Stay within the domain of finance"
    ]
}

 

Step 9: Validate and Adjust the Normalized Context

 

Before feeding the structured context into MCP, validate accuracy and completeness against the expected operation of the language model. Adjust as necessary to optimize performance and predictability.

 

Step 10: Inject Normalized Context into MCP

 

Use the validated and structured context as input to the MCP, ensuring the language model can interpret it to perform tasks effectively. This step finalizes the preparation, enabling more structured and predictable interactions with the model.