Show HN: CodePrism – an AI-generated code analysis engine as MCP

I wanted to see how far I could push the idea of autonomous software development. So I ran a (slightly reckless) experiment: Could an AI build a real, working code intelligence tool entirely on its own — without any human code? Over the span of a week — mostly while I was doing my day job — I let a custom AI agent (built with Dragonscale) design, write, and document a full static analysis engine from scratch. No human-written code. No human-designed features. Just the AI, asking itself questions like: "What tools would help me understand a repo?" "How should I explain a function’s purpose?" "What format should I use to talk to another AI?" It came up with 18+ tools for explaining symbols, tracing data flows, detecting patterns, and analyzing complexity. It writes natural-language summaries and exposes a full JSON-RPC 2.0 interface via the Model Context Protocol (MCP). The result: CodePrism — a fully AI-generated, LLM-integrated static analysis server. I’ve been using it inside Cursor, Copilot, and VS Code — and surprisingly, it works. It gives me ~10x faster insights into unfamiliar Python codebases, and often surfaces subtle structure I would've missed. Links: Homepage + blog: https://rustic-ai.github.io/codeprism https://rustic-ai.github.io/codeprism GitHub repo: https://github.com/rustic-ai/codeprism https://github.com/rustic-ai/codeprism This is still an experiment. No guarantees. It might break. But it’s also kind of fun. If you're curious about the boundaries of AI-autonomous tooling, check it out — and feel free to get involved (no code PRs, please ). Happy to answer questions and share more about the setup, agents, or architecture.