How to queue tool outputs and progressively build MCP state?

 

Step 1: Understand the Components of MCP

 

Before you begin implementing MCP, ensure you understand its various components:

 

Step 2: Define System Instructions

 

The system instructions guide the language model on its role. For example:

You are a helpful assistant specialized in finance. Always prioritize accuracy and clarity in your explanations.

 

Step 3: Establish User Profile

 

Define the user’s name, preferences, and goals:

User Name: John Doe
Preferences: Conversational, brief responses
Goals: Learn about investment strategies

 

Step 4: Configure Document Context

 

Provide the necessary document context, such as a knowledge base or recent uploads:

Knowledge Base: "finance_guide.pdf"
Recent Upload: "stockmarketanalysis.csv"

 

Step 5: Determine Active Tasks/Goals

 

Identify the current tasks or objectives the model should focus on:

Current Objective: Research sustainable investment options
To-Do: Compare investment strategies

 

Step 6: Specify Tool Access

 

Outline what tools the model can utilize:

Tool Access: Web search API, Python script execution, Database query

 

Step 7: Set Rules/Constraints

 

Define rules to guide the language model's behavior:

Constraints: Never suggest medical diagnoses, avoid political discussions

 

Step 8: Implement the MCP Structure

 

Organize the above components into a structured format to build the MCP:

{
  "system_instructions": "You are a helpful assistant specialized in finance.",
  "user_profile": {
    "name": "John Doe",
    "preferences": "Conversational, brief responses",
    "goals": "Learn about investment strategies"
  },
  "document_context": [
    {
      "type": "knowledge_base",
      "name": "finance_guide.pdf"
    },
    {
      "type": "recent_upload",
      "name": "stockmarketanalysis.csv"
    }
  ],
  "activetasksgoals": [
    {
      "current_objective": "Research sustainable investment options"
    },
    {
      "to-do": "Compare investment strategies"
    }
  ],
  "tool_access": [
    "Web search API",
    "Python script execution",
    "Database query"
  ],
  "rules_constraints": [
    "Never suggest medical diagnoses",
    "Avoid political discussions"
  ]
}

 

Step 9: Queue Tool Outputs and Progressively Build MCP State

 

As the model processes tasks and interacts with tools, queue the outputs:

output_queue = []

def processtooloutput(tool_output):
    outputqueue.append(tooloutput)
    # Further processing logic

Example of adding tool outputs to the queue
tooloutput1 = "Web search result on sustainable investments"
tooloutput2 = "Python script output on predictive analysis"
processtooloutput(tooloutput1)
processtooloutput(tooloutput2)

 

Step 10: Continuously Update MCP with New Context

 

With each interaction or task completion, update the MCP structure dynamically:

def updatemcpcontext(new_info):
    # Example function to update MCP context
    mcpjson['activetasksgoals'].append(newinfo)

Updating context with new task
new_task = {
    "current_objective": "Assess impact of economic news on stock prices"
}
updatemcpcontext(new_task)

 

By following these steps, you can methodically queue tool outputs and progressively build an MCP state suitable for managing interactions with language models effectively.