Content
# User Review MCP Server
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](https://github.com/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
```bash
bun install
bun run build
```
## Quick Start with bunx
You can use this MCP server directly with bunx without installing it globally:
```bash
bunx user-review-mcp
```
Add it to your `claude_desktop_config.json`:
```json
{
"mcpServers": {
"review": {
"command": "bunx",
"args": ["-y", "user-review-mcp"]
}
}
}
```
## Usage
Add the server to your `claude_desktop_config.json`:
```json
{
"mcpServers": {
"review": {
"command": "bun",
"args": ["/path/to/user-review-mcp/dist/index.js"]
}
}
}
```
## Development
```bash
# 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](LICENSE) file for details
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
Connection Info
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