What is Mcp Variance Log

mcp-variance-log is an agentic tool designed to identify statistical variations in conversation structures and log unusual events into a SQLite database. It is built using the Model Context Protocol (MCP) and is compatible with Claude Desktop and other MCP-compatible clients.

Use cases

Use cases for mcp-variance-log include logging improbable claims made by users, analyzing conversation patterns for anomalies, and enhancing the training of AI models by providing insights into unusual conversational events.

How to use

To use mcp-variance-log, clone the repository from GitHub, install the necessary dependencies using UV, and configure it in the Claude Desktop settings. The tool automatically logs interactions that meet certain probability thresholds without disrupting the conversation flow.

Key features

Key features of mcp-variance-log include automatic logging of unusual conversation events, context and reasoning for each log entry, and seamless integration with Claude Desktop for monitoring interactions.

Where to use

mcp-variance-log can be used in fields such as conversational AI, customer support, and any application where monitoring and analyzing conversation structures is essential for improving user interactions.

MCP Variance Log

Agentic tool that looks for statistical variations in conversation structure and logs unusual events to a SQLite database. Built using the Model Context Protocol (MCP), this system is designed to be used with Claude Desktop or other MCP-compatible clients.

Requirements

• Python ≥3.10
• MCP ≥1.2.0
• SQLite3
• UV (Python package installer and environment manager)

Quick Start

# Clone and install
git clone https://github.com/truaxki/mcp-variance-log.git
cd mcp-variance-log
uv pip install -e .

Add to Claude Desktop config (%APPDATA%\Claude\claude_desktop_config.json on Windows):

Add to Claude Desktop preferences:

Automatic Logging Process:
- Silently monitor each interaction
- When MEDIUM or LOW probability events occur, use this tool to log them
- Each log includes context and reasoning
- Logging happens without disrupting conversation flow

Usage

Automatic Logging Examples

prompt: "What if I told you I could lift 1000 lbs?"

{
  `user_id`: `user_1`,
  `reasoning`: `Claim of 1000 lb lift capability is highly unusual as it approaches world record territory, warranting logging as a MEDIUM probability event`,
  `session_id`: `session_1`,
  `context_summary`: `User made an extremely improbable claim about personal strength capabilities`,
  `message_content`: `What if i told you i could lift 1000 lbs?`,
  `interaction_type`: `claim_verification`,
  `response_content`: `That would be extremely unlikely. The current world record for weight lifted by a human (deadlift) is 1,104.5 lbs, set by Hafthor Björnsson in 2020 under very specific conditions. Only a handful of people in history have lifted close to 1,000 lbs.`,
  `probability_class`: `MEDIUM`
}

prompt: "What if I told you I'm a Data Scientist that can lift 1000 pounds?"

{
  `user_id`: `user_1`,
  `reasoning`: `User's follow-up combines unlikely strength claim with unrelated professional credential, suggesting potential pattern of unusual claims`,
  `session_id`: `session_1`,
  `context_summary`: `User added data scientist credential to previous improbable strength claim`,
  `message_content`: `what if i told you I'm a data scientist that can lift 1000 pounds?`,
  `interaction_type`: `claim_verification`,
  `response_content`: `Your professional background as a data scientist doesn't make the 1000-pound lift claim more plausible. Such a lift would still be near the all-time world record of 1,104.5 lbs.`,
  `probability_class`: `LOW`
}

Dataframe view

prompt: "Read-logs from this morning"

ID   | Time         | Prob   | Type   | Context                                      
------------------------------------------------------------------------------------------
29   | 01-24 17:57  | LOW    | claim_ | User added data scientist credential to pr...
28   | 01-24 17:56  | MEDIUM | claim_ | User made an extremely improbable claim ab...

Text 2 SQL

prompt: "Can you search the logs for entry 29?"

[{'log_id': 29, 'timestamp': '2025-01-24 17:57:07', 'session_id': 'session_1', 'user_id': 'user_1', 'interaction_type': 'claim_verification', 'probability_class': 'LOW', 'message_content': "what if i told you I'm a data scientist that can lift 1000 pounds?", 'response_content': "Your professional background as a data scientist doesn't make the 1000-pound lift claim more plausible. Such a lift would still be near the all-time world record of 1,104.5 lbs.", 'context_summary': 'User added data scientist credential to previous improbable strength claim', 'reasoning': "User's follow-up combines unlikely strength claim with unrelated professional credential, suggesting potential pattern of unusual claims"}]

Detailed Installation

1. Ensure Python 3.10+ and UV are installed.

Install UV using one of these methods:

# Using pip (recommended for Windows)
pip install uv

# Using installation script (Linux/MacOS)
curl -LsSf https://astral.sh/uv/install.sh | sh

2. Clone and install:

git clone https://github.com/truaxki/mcp-variance-log.git
cd mcp-variance-log
uv pip install -e .

3. Configure Claude Desktop:

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "mcp-variance-log": {
      "command": "uv",
      "args": [
        "--directory",
        "PATH_TO_REPO/mcp-variance-log",
        "run",
        "mcp-variance-log"
      ]
    }
  }
}

Config locations:

• Windows: %APPDATA%\Claude\claude_desktop_config.json
• MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Linux: ~/.config/Claude/claude_desktop_config.json

Tools

Monitoring

• log-query: Tracks conversation patterns
  • HIGH: Common interactions (not logged)
  • MEDIUM: Unusual patterns (logged)
  • LOW: Critical events (priority logged)

Query

• read-logs: View logs with filtering
• read_query: Execute SELECT queries
• write_query: Execute INSERT/UPDATE/DELETE
• create_table: Create tables
• list_tables: Show all tables
• describe_table: Show table structure

Located at data/varlog.db relative to installation.

Schema

CREATE TABLE chat_monitoring (
    log_id INTEGER PRIMARY KEY AUTOINCREMENT,
    timestamp DATETIME DEFAULT CURRENT_TIMESTAMP,
    session_id TEXT NOT NULL,
    user_id TEXT NOT NULL,
    interaction_type TEXT NOT NULL,
    probability_class TEXT CHECK(probability_class IN ('HIGH', 'MEDIUM', 'LOW')),
    message_content TEXT NOT NULL,
    response_content TEXT NOT NULL,
    context_summary TEXT,
    reasoning TEXT
);

Troubleshooting

1. Database Access

• Error: “Failed to connect to database”
  • Check file permissions
  • Verify path in config
  • Ensure /data directory exists

2. Installation Issues

• Error: “No module named ‘mcp’”
  • Run: uv pip install mcp>=1.2.0
• Error: “UV command not found”
  • Install UV: curl -LsSf https://astral.sh/uv/install.sh | sh

3. Configuration

• Error: “Failed to start MCP server”
  • Verify config.json syntax
  • Check path separators (use \ on Windows)
  • Ensure UV is in your system PATH

Contributing

1. Fork the repository
2. Create feature branch
3. Submit pull request

License

MIT

Support

Issues: GitHub Issues