I built T-Zero V3, an open-source MCP Server that cures LLM hallucinations and cuts token context costs by up to 95% 🚀

说明与用途

T‑Zero V3 将令牌使用量降低至 95% 并通过仅提供代码骨架来消除幻觉，提升大型项目的编码效率。

问题

AI coding agents on large codebases suffer from token exhaustion and hallucinations due to overwhelming context.

结果

T‑Zero V3 cuts token costs by up to 95% and eliminates hallucinations by providing only the code skeleton to the LLM, enabling efficient and focused AI coding on large projects.

工作流

• 1. Install T‑Zero V3 and embed its MCP server.
• 2. Add a 1‑line JSON config to the AI agent (Cursor, Claude Desktop, Antigravity IDE).
• 3. The agent generates a T‑Zero context tree of the workspace.
• 4. Query cross‑module dependency graphs, audit for code smells, enforce zero‑leak boundaries.
• 5. The agent interacts with the LLM using the trimmed context, avoiding hallucinations and saving tokens.

证据

Token Exhaustion: Sending your entire workspace into the context window drains your API budget instantly.

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LLM Hallucinations: When AI gets overwhelmed with thousands of lines of irrelevant implementation logic, it loses focus, hallucinates, and breaks your architectural boundaries.

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It uses an AST-based static analyzer to strip out all the "fat" (long function implementations) while keeping the absolute "skeleton" (class/method signatures, docstrings, decorators, and imports).

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reducing token usage by up to 95% while maintaining 100% semantic fidelity.

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By simply adding a 1-line JSON config to Cursor, Claude Desktop, or Antigravity IDE, you give your AI agent access to 13 autonomous tools.

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