UnravelAI MCP server enforces AST-grounded, hallucination-free bug diagnosis

UnravelAI, published on GitHub by EruditeCoder108, is an MCP server built around a deterministic AST (Abstract Syntax Tree) evidence engine. Rather than letting AI agents reason backwards from crash symptoms — the failure mode the project identifies as the root cause of most LLM debugging errors — UnravelAI forces agents to ground their diagnoses in verified execution facts extracted directly from code structure. The project is compatible with Claude Code, Gemini CLI, Cursor, and any MCP-compatible agent.

A key architectural feature is that every verified diagnosis is embedded and stored in a semantic archive.

A key architectural feature is that every verified diagnosis is embedded and stored in a semantic archive. When a similar bug appears later, the engine returns an archive hit before the agent even reads a file, meaning the system accumulates a track record and improves over time. The repository includes dedicated directories for the MCP server (`unravel-mcp`), a VS Code extension (`unravel-vscode`), validation tooling, research, and planning documents including a roadmap and local LLM variant plan, suggesting the project is actively evolving toward broader deployment scenarios.

Key facts

1. 01UnravelAI is an open-source MCP server posted to GitHub by EruditeCoder108.
2. 02It uses a deterministic AST evidence engine to extract verified structural facts from code.
3. 03The core problem it addresses: LLMs debug by reasoning backwards from crash symptoms, missing where data was first corrupted.
4. 04Compatible with Claude Code, Gemini CLI, Cursor, and any MCP-compatible agent.
5. 05Every verified diagnosis is embedded and stored in a semantic archive for future reuse.
6. 06The repository includes a VS Code extension (`unravel-vscode`), an MCP module (`unravel-mcp`), and a local LLM variant plan.
7. 07The repo has 14 stars and 1 fork at time of publication.

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