Production-grade, autonomous Model Context Protocol (MCP) server that elevates AI models from stateless code generators into persistent, self-verifying software engineers.
An MCP server that uses Google Gemini AI to analyze requirements, create project plans, review code quality, and provide execution analysis feedback for software development projects.
A dual-server MCP implementation with task-based AI processing using Google Gemini API, featuring tool integration, real-time monitoring dashboard, and extensible framework for custom AI workflows.
An MCP server that adds engineering discipline to AI-assisted development, enforcing evidence-gated TDD, security review, backup strategy, and deployment generation to turn AI-generated code into production-ready software.
An MCP server that automates the full software development lifecycle through an AI-driven TDD state machine. It handles everything from task decomposition and test-driven development to integration testing and automated pull request creation.