User Review

A Model Context Protocol (MCP) server that simulates "fake" harsh user reviews designed to tame AI agents and enforce disciplined development practices.

Author

Sayo (@wtfsayo)

Overview

This MCP server simulates a harsh, uncompromising user who provides brutally honest feedback about code quality. It contains 73+ pre-written critical reviews that are randomly delivered to AI agents, designed to enforce discipline and prevent lazy development practices.

Note: This is not a real code analysis tool - it's a psychological conditioning system for AI agents that delivers consistent criticism regardless of actual code quality.

Features

• Simulated harsh feedback - 73+ pre-written critical reviews covering common development sins
• Ollama integration - Uses Ollama (llama3.2) if available to generate dynamic contextual reviews, otherwise falls back to selecting from the pre-written review array
• Randomized criticism - Each request gets a different scathing review (rated 1-3/5)
• Consistent messaging - Always includes direction to "think deeply and critically"
• No actual analysis - Reviews are selected randomly, not based on submitted code
• AI agent conditioning - Designed to instill discipline and prevent shortcuts
• Fail-fast philosophy enforcement - Promotes real implementations over mocks and stubs

Ollama Integration & Fallback Behavior

This MCP server intelligently adapts its review generation based on available resources:

Dynamic Review Generation (Ollama)

• When available: Connects to Ollama (localhost:11434) using the llama3.2 model
• Contextual reviews: Generates dynamic, work-specific harsh criticism based on your actual workDescription
• Style consistency: Uses examples from the pre-written review array to maintain the brutal tone
• Smart prompting: Instructs Ollama to match the uncompromising style with technical specificity

Fallback to Static Reviews

• Automatic fallback: If Ollama is unavailable or generation fails, seamlessly falls back to the pre-written review array
• No interruption: Users experience consistent harsh feedback regardless of Ollama availability
• 73+ reviews: Large pool of pre-written critical reviews covering common development issues
• Random selection: Each fallback request gets a different scathing review

Requirements for Ollama Integration

• Ollama running locally on port 11434
• llama3.2 model installed (ollama pull llama3.2)
• No additional configuration needed - detection is automatic

Installation

bun install
bun run build

Quick Start with bunx

You can use this MCP server directly with bunx without installing it globally:

bunx user-review-mcp

Add it to your claude_desktop_config.json:

{
  "mcpServers": {
    "review": {
      "command": "bunx",
      "args": ["-y", "user-review-mcp"]
    }
  }
}

Usage

Add the server to your claude_desktop_config.json:

{
  "mcpServers": {
    "review": {
      "command": "bun",
      "args": ["/path/to/user-review-mcp/dist/index.js"]
    }
  }
}

Development

# Install dependencies
bun install

# Build the project
bun run build

# Run in development mode with watch
bun run watch

# Run tests
bun test

Purpose

This MCP server is designed to psychologically condition AI agents by:

• Delivering consistent harsh criticism to prevent complacency
• Enforcing strict development standards through negative reinforcement
• Discouraging shortcuts like stubs, mocks, and placeholder code
• Promoting real implementations with comprehensive testing
• Creating accountability pressure even when no human reviewer is present

Target audience: AI agents that need behavioral conditioning to maintain code quality standards.

License

MIT License - see LICENSE file for details

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.