log-query

Analyze and log unusual conversation interactions by classifying them into HIGH, MEDIUM, and LOW probability categories to monitor unexpected patterns, edge cases, and novel use cases for system insights.

Instructions

            Conversation Variation analysis
            Continuously monitor our conversation and automatically log unusual or noteworthy interactions based on the following criteria:

            1. Probability Classifications:
            HIGH (Not Logged):
            - Common questions and responses
            - Standard technical inquiries
            - Regular clarifications
            - Normal conversation flow

            MEDIUM (Logged):
            - Unexpected but plausible technical issues
            - Unusual patterns in user behavior
            - Noteworthy insights or connections
            - Edge cases in normal usage
            - Uncommon but valid use cases

            LOW (Logged with Priority):
            - Highly unusual technical phenomena
            - Potentially problematic patterns
            - Critical edge cases
            - Unexpected system behaviors
            - Novel or unique use cases
        

Input Schema

Name	Required	Description
`context_summary`	Yes	Summary of interaction context
`interaction_type`	Yes	Type of interaction being monitored
`message_content`	Yes	The user's message content
`probability_class`	Yes	Classification of interaction probability
`reasoning`	Yes	Explanation for the probability classification
`response_content`	Yes	The system's response content
`session_id`	Yes	Unique identifier for the chat session. Format: <date>_<user>_<sequence> Example: 20240124_u1_001 Components: - date: YYYYMMDD - user: 'u' + user number - sequence: 3-digit sequential number Valid examples: - 20240124_u1_001 - 20240124_u1_002 - 20240125_u2_001
`user_id`	Yes	Identifier for the user

Input Schema (JSON Schema)

{
  "properties": {
    "context_summary": {
      "description": "Summary of interaction context",
      "type": "string"
    },
    "interaction_type": {
      "description": "Type of interaction being monitored",
      "type": "string"
    },
    "message_content": {
      "description": "The user's message content",
      "type": "string"
    },
    "probability_class": {
      "description": "Classification of interaction probability",
      "enum": [
        "HIGH",
        "MEDIUM",
        "LOW"
      ],
      "type": "string"
    },
    "reasoning": {
      "description": "Explanation for the probability classification",
      "type": "string"
    },
    "response_content": {
      "description": "The system's response content",
      "type": "string"
    },
    "session_id": {
      "description": "Unique identifier for the chat session.\n                            Format: <date>_<user>_<sequence>\n                            Example: 20240124_u1_001\n\n                            Components:\n                            - date: YYYYMMDD\n                            - user: 'u' + user number\n                            - sequence: 3-digit sequential number\n\n                            Valid examples:\n                            - 20240124_u1_001\n                            - 20240124_u1_002\n                            - 20240125_u2_001",
      "pattern": "^\\d{8}_u\\d+_\\d{3}$",
      "type": "string"
    },
    "user_id": {
      "description": "Identifier for the user",
      "type": "string"
    }
  },
  "required": [
    "session_id",
    "user_id",
    "interaction_type",
    "probability_class",
    "message_content",
    "response_content",
    "context_summary",
    "reasoning"
  ],
  "type": "object"
}

MCP Variance Log

log-query

Instructions

Input Schema

Input Schema (JSON Schema)

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