Mibayy

Mibayy / token-savior

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Token-efficient structural codebase MCP server for AI-assisted development

36
3
100% credibility
Found Mar 31, 2026 at 36 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Token Savior indexes codebases structurally to provide AI coding assistants with precise, token-efficient queries for navigation, dependencies, edits, and testing.

How It Works

1
🔍 Discover Token Savior

You hear about a helpful tool that lets your AI coding buddy quickly pinpoint exact code pieces instead of scanning whole folders.

2
📥 Add to your project

You place the tool in your work folder with a simple copy – it scans your code automatically and builds a smart guide.

3
🔗 Link to your AI helper

You tell your AI coding app to use this new smart guide, like adding a favorite shortcut.

4
💡 Ask smart questions

Your AI now asks 'where's this function?' or 'what breaks if I change that?' and gets tiny, perfect answers in seconds.

5
✏️ Make safe changes

You swap code bits or add new ones precisely, then check only the affected tests run fast.

🎉 Code smarter, faster

Your AI works magic with way less effort, changes are safer, and sessions cost less – your projects speed up!

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Star Growth

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AI-Generated Review

What is token-savior?

Token-savior is a Python MCP server that indexes your codebase structurally for AI-assisted development, letting agents query precise snippets—like a function's source or transitive change impacts—instead of entire files. It slashes token usage by 99% in real sessions, serving up 67 chars for a symbol search that would otherwise cost megabytes. Supports Python, TypeScript/JS, Go, Rust, C#, Markdown, and JSON via a zero-dependency setup that syncs with git changes automatically.

Why is it gaining traction?

Unlike LSPs that stop at "where is this defined," token-savior delivers full impact analysis (154 direct + 492 transitive deps in 0.45ms on CPython) and 34 tools for navigation, git summaries, safe edits, and impacted tests. Developers hook it into Claude Code, Cursor, or Cline via simple MCP config, seeing instant token savings and a live dashboard tracking efficiency. Multi-project workspaces and sub-millisecond queries make it a scalpel for bloated AI coding contexts.

Who should use this?

Backend devs refactoring Python or Rust monorepos, full-stack teams with TS/Go/C# stacks hitting context limits in Cursor sessions, or AI agent builders needing precise codebase queries without grep/cat overhead. Ideal for anyone using MCP clients like Windsurf or Hermes on mid-to-large projects (FastAPI to CPython-scale).

Verdict

Grab it if you're deep in AI-assisted development—solid docs and benchmarks make setup trivial, despite alpha status and 36 stars (1.0% credibility). Test on a real project; token wins are immediate, but watch for live-edit sync quirks in fast iteration.

(198 words)

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