user-review-mcp

# 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.