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

Description and use

T‑Zero V3 cuts token usage by up to 95 % and eliminates hallucinations by supplying the LLM with only the code skeleton, improving large‑project coding efficiency.

Problem

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

Outcome

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.

Workflow

• 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.

Evidence

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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