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MCP Variance Log
by truaxki

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)

Related MCP server: QueryForge

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):

{
  "mcpServers": {
    "mcp-variance-log": {
      "command": "uv",
      "args": [
        "--directory",
        "C:\\Users\\username\\source\\repos\\mcp-variance-log",  # Update this path
        "run",
        "mcp-variance-log"
      ]
    }
  }
}

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
  1. Clone and install:

git clone https://github.com/truaxki/mcp-variance-log.git
cd mcp-variance-log
uv pip install -e .
  1. 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

  1. 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

  1. 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

Available Tools

8 tools
append_insightB

Add a business insight to the memo

Inputs: insight (required).

create_tableC

Create a new table in the SQLite database

Inputs: query (required).

describe_tableC

Show structure of a specific table

Inputs: table_name (required).

list_tablesA

List all tables in the database

Inputs: no named parameters.

log-queryC
            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
        

Inputs: session_id (required), user_id (required), interaction_type (required), probability_class (required), +4 more.

read-logsB

Retrieve logged conversation variations from the database.

Inputs: limit (required), start_date, end_date, full_details.

read_queryA

Execute a SELECT query on the SQLite database

            Schema Reference:
            Table: chat_monitoring
            Fields:
            - log_id (INTEGER PRIMARY KEY)
            - timestamp (DATETIME)
            - session_id (TEXT)
            - user_id (TEXT)
            - interaction_type (TEXT)
            - probability_class (TEXT: HIGH, MEDIUM, LOW)
            - message_content (TEXT)
            - response_content (TEXT)
            - context_summary (TEXT)
            - reasoning (TEXT)

            Example:
            SELECT timestamp, probability_class, context_summary 
            FROM chat_monitoring 
            WHERE probability_class = 'LOW'
            LIMIT 5;
            

Inputs: query (required).

write_queryC

Execute an INSERT, UPDATE, or DELETE query

Inputs: query (required).

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    C2.8/5.0

    Scored across 8 tools

    Disambiguation3/5

    Most tools have distinct purposes (e.g., create_table vs. list_tables), but there is overlap between log-query and read-logs, as both involve logging/retrieving conversation variations, which could cause confusion. Additionally, read_query and write_query are clearly distinct from each other but share the database query domain with other tools like describe_table.

    Naming Consistency2/5

    Naming is inconsistent with mixed conventions: some use snake_case (append_insight, create_table), others use kebab-case (log-query, read-logs), and some are unclear (read_query vs. write_query, which are snake_case but differ in verb style). There is no uniform pattern across all tools, making it harder to predict naming.

    Tool Count4/5

    With 8 tools, the count is reasonable for a server focused on database operations and conversation logging. It covers core functions without being overly bloated, though the scope might feel slightly broad due to mixing database management with logging features.

    Completeness3/5

    For database operations, there is good coverage (create, list, describe, read, write), but lacks update/delete specific tools, relying on write_query for those. For conversation logging, it has logging and retrieval, but no direct management tools like delete_logs or update_logs, leaving minor gaps in the lifecycle.

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