Cross-Review MCP

Example Use Cases

• 1

  Simultaneous expert advice from multiple AI models to improve accuracy and reliability.

• 2

  Automated fallback and replacement of failing AI models to maintain service continuity.

• 3

  Detailed logging and analytics to optimize AI review workflows and costs.

Description

Cross-Review MCP enables simultaneous querying of five AI models (OpenAI, Gemini, DeepSeek, Mistral, OpenRouter) to get diverse expert perspectives and a consensus verdict quickly. It supports various review types such as security, performance, correctness, style, and general advice, with advanced features like adaptive fallback, model discovery, hot-swapping, and comprehensive query logging. The server can run locally or remotely with HTTP and stdio modes, providing a live dashboard and detailed analytics for optimization and debugging.