howardpen9

howardpen9 / kimi-code-mcp

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MCP server bridging Kimi Code (256K context) with Claude Code

13
2
100% credibility
Found Mar 03, 2026 at 13 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
TypeScript
AI Summary

This repository provides a connector that enables Claude Code to delegate large-scale code reading and analysis tasks to the more cost-effective Kimi Code AI.

How It Works

1
🔍 Discover a cost-saving trick

You're working on a big coding project and notice your AI helper is charging a lot just to read all your files, so you find this smart bridge that lets a cheaper AI do the heavy reading.

2
📝 Sign up for the budget AI

Get a free trial account with Kimi, the efficient AI that can read huge amounts of code without costing much.

3
🔗 Set up the bridge

Download the simple connector and link it to your main AI helper (Claude) so they can work together seamlessly.

4
💬 Ask for a deep code review

Tell Claude to analyze your project's architecture, find bugs, or plan changes—it automatically hands off the big reading job to Kimi.

5
⚡ Watch the magic happen

Kimi scans your entire project in one go while Claude waits, then they team up to give you clear insights and suggestions.

🎉 Save money and get better results

Enjoy thorough code reviews, security checks, and refactoring plans at a fraction of the usual cost, with your AI duo getting smarter each time.

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Star Growth

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AI-Generated Review

What is kimi-code-mcp?

This TypeScript MCP server bridges Kimi Code's 256K context model with Claude Code over the Model Context Protocol, letting Claude orchestrate tasks while Kimi handles bulk codebase reading. It solves Claude's high token costs for scanning large repos by delegating analysis to Kimi's cheap CLI, returning structured reports via tools like kimi_analyze and kimi_resume. Setup involves npm build and .mcp.json config for VSCode or global use.

Why is it gaining traction?

It stands out in the mcp github copilot vscode scene by compressing 200K+ token reads into 5-15K summaries, saving 60-80% on Claude bills—perfect for mcp server ai workflows. Features like session resumption and detail levels (summary/normal/detailed) enable iterative audits without re-reading, unlike basic mcp server examples. Developers hook on the AI pair review: Kimi scans, Claude edits.

Who should use this?

Backend teams auditing mcp github issues or dependencies in large Python/TypeScript repos via mcp github python or mcp server list. Security-focused mcp github project managers pre-scanning for vulnerabilities, or n8n/SAP integrators needing mcp server tutorial for 256k context. Skip if your codebase is under 50 files.

Verdict

Early maturity at 13 stars and 1.0% credibility score, but excellent bilingual docs make it easy to adopt as an mcp github server. Try it on a non-critical project if you have a Kimi subscription—strong token ROI potential despite low traction.

(198 words)

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