MCP Variance Log

Instructions

Implementation Reference

• src/mcp_variance_log/server.py:329-355 (handler)

  Handler for the 'log-query' tool: extracts input arguments and delegates to LogDatabase.add_log method.
  elif name == "log-query": # Existing log-query logic session_id = arguments.get("session_id", "") user_id = arguments.get("user_id", "") interaction_type = arguments.get("interaction_type", "") probability_class = arguments.get("probability_class", "") message_content = arguments.get("message_content", "") response_content = arguments.get("response_content", "") context_summary = arguments.get("context_summary", "") reasoning = arguments.get("reasoning", "") success = db.add_log( session_id=session_id, user_id=user_id, interaction_type=interaction_type, probability_class=probability_class, message_content=message_content, response_content=response_content, context_summary=context_summary, reasoning=reasoning ) return [types.TextContent( type="text", text="Log entry added successfully" if success else "Failed to add log entry" )],
• src/mcp_variance_log/server.py:57-117 (schema)

  JSON Schema defining the input parameters and validation for the 'log-query' tool.
  inputSchema={ "type": "object", "properties": { "session_id": { "type": "string", "description": """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""", "pattern": "^\\d{8}_u\\d+_\\d{3}$" # Regex pattern to validate format }, "user_id": { "type": "string", "description": "Identifier for the user" }, "interaction_type": { "type": "string", "description": "Type of interaction being monitored" }, "probability_class": { "type": "string", "enum": ["HIGH", "MEDIUM", "LOW"], "description": "Classification of interaction probability" }, "message_content": { "type": "string", "description": "The user's message content" }, "response_content": { "type": "string", "description": "The system's response content" }, "context_summary": { "type": "string", "description": "Summary of interaction context" }, "reasoning": { "type": "string", "description": "Explanation for the probability classification" } }, "required": [ "session_id", "user_id", "interaction_type", "probability_class", "message_content", "response_content", "context_summary", "reasoning" ] },
• src/mcp_variance_log/server.py:30-118 (registration)

  Tool registration in the @server.list_tools() handler, specifying name, description, and schema.
  types.Tool( name="log-query", description=""" 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 """, inputSchema={ "type": "object", "properties": { "session_id": { "type": "string", "description": """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""", "pattern": "^\\d{8}_u\\d+_\\d{3}$" # Regex pattern to validate format }, "user_id": { "type": "string", "description": "Identifier for the user" }, "interaction_type": { "type": "string", "description": "Type of interaction being monitored" }, "probability_class": { "type": "string", "enum": ["HIGH", "MEDIUM", "LOW"], "description": "Classification of interaction probability" }, "message_content": { "type": "string", "description": "The user's message content" }, "response_content": { "type": "string", "description": "The system's response content" }, "context_summary": { "type": "string", "description": "Summary of interaction context" }, "reasoning": { "type": "string", "description": "Explanation for the probability classification" } }, "required": [ "session_id", "user_id", "interaction_type", "probability_class", "message_content", "response_content", "context_summary", "reasoning" ] }, ),
• src/mcp_variance_log/db_utils.py:73-107 (helper)

  Core helper function in LogDatabase that performs the SQL INSERT to store the log data.
  def add_log(self, session_id: str, user_id: str, interaction_type: str, probability_class: str, message_content: str, response_content: str, context_summary: str, reasoning: str) -> bool: """ Add a new log entry to the database. Args: session_id (str): Unique identifier for the chat session user_id (str): Identifier for the user interaction_type (str): Type of interaction being monitored probability_class (str): Classification (HIGH, MEDIUM, LOW) message_content (str): The user's message content response_content (str): The system's response content context_summary (str): Summary of interaction context reasoning (str): Explanation for the classification Returns: bool: True if successful, False otherwise """ try: with sqlite3.connect(self.db_path) as conn: cursor = conn.cursor() cursor.execute(''' INSERT INTO chat_monitoring ( session_id, user_id, interaction_type, probability_class, message_content, response_content, context_summary, reasoning ) VALUES (?, ?, ?, ?, ?, ?, ?, ?) ''', (session_id, user_id, interaction_type, probability_class, message_content, response_content, context_summary, reasoning)) return True except Exception as e: print(f"Error adding log: {e}") return False