How to track completion status of subtasks inside MCP?

 

Step 1: Understand MCP's Role

 

MCP (Model Context Protocol) serves as a "blueprint" to ensure the AI language models function predictably and effectively. It helps in structuring and transmitting context, thereby making the models more reliable and adaptable to various situations.

 

Step 2: Identify Subtasks and Define Completion Criteria

 

To track the completion status of subtasks within MCP, first, you need to clearly identify the subtasks and define what constitutes their completion. Each subtask should be measurable and objective.

 

Step 3: Structure Subtasks in the MCP

 

Integrate the defined subtasks within the MCP by adding them to the context under Active Tasks/Goals. Here's a simplified representation:

{
  "active_tasks": [
    {
      "taskid": "maintask_1",
      "description": "Complete main task",
      "subtasks": [
        {
          "subtaskid": "subtask1A",
          "description": "Research",
          "status": "incomplete"
        },
        {
          "subtaskid": "subtask1B",
          "description": "Draft initial report",
          "status": "incomplete"
        }
      ]
    }
  ]
}

 

Step 4: Implement a Tracking Mechanism

 

Design a system or use software that can periodically check and update the status of each subtask. This can be programmed based on certain triggers or checks:

def updatesubtaskstatus(taskid, subtaskid, new_status):
    task = findtask(taskid)
    subtask = findsubtask(task, subtaskid)
    if subtask:
        subtask['status'] = new_status

 

Step 5: Update the MCP Context

 

Ensure any changes in task completion status are reflected back into the MCP context. This helps in maintaining an accurate status:

updatesubtaskstatus("maintask1", "subtask_1A", "complete")
updatesubtaskstatus("maintask1", "subtask1B", "inprogress")

 

Step 6: Provide Feedback or Alerts

 

Incorporate a system to alert users or provide feedback when specific subtasks are updated. This can include logging changes or sending notifications:

def notifyuser(subtaskid, status):
    print(f"Subtask {subtask_id} updated to {status}")

 

Step 7: Review and Reflect on Subtask Completion

 

Periodically review the MCP context to assess progress and determine if further adjustments to subtasks or the overall goal are necessary. This also involves checking if all completion criteria are met.

 

By following this structured guide, you can efficiently track and manage subtask completion within the Model Context Protocol (MCP).