yfedoseev

yfedoseev / fossil-mcp

Public

The code quality toolkit for the agentic AI era. Find dead code, clones, and scaffolding across 15 languages. MCP server + CLI.

fossil-mcp.com ai-coding ai-tools ast claude-code cli
46
2
100% credibility
Found Feb 10, 2026 at 20 stars 2x -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Rust
AI Summary

Fossil MCP is a code analysis tool designed for AI-generated projects that uncovers dead code, duplicates, scaffolding remnants, and temporary files across 15 languages, usable via command line or as a server for AI coding tools.

How It Works

1
📰 Discover Fossil MCP

You hear about a handy tool that spots mess from AI-written code like unused bits, copies, and leftover notes.

2
📥 Grab the program

Download the ready-to-run file for your computer and put it where you can easily use it.

3
📂 Check your project

Run the program on your code folder and let it scan everything quickly.

4
📊 See the cleanup list

Get a colorful dashboard showing dead code, duplicates, and AI scraps with confidence levels and file hotspots.

5
🔍 Explore or share results

Dive into details with the interactive guide or save a report to share with your team.

6
Use with AI helper?
✅
Connect to AI

Your AI now self-checks code before suggesting changes.

🧹
Clean manually

Follow the guide to remove issues step by step.

✨ Code is clean and solid

Your project runs smoothly without hidden messes, saving time and headaches.

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Star Growth

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AI-Generated Review

What is fossil-mcp?

Fossil-mcp is a Rust-built CLI and MCP server for code quality analysis in GitHub repositories bloated by AI tools like GitHub Copilot. It detects dead code, structural clones, and scaffolding artifacts—think orphaned helpers, Phase 1 comments, and temp files—across 15 languages including Python, JS/TS, Rust, and Java. Run `fossil-mcp scan` for a dashboard or SARIF output integrable with GitHub Actions or GitLab CI YAML.

Why is it gaining traction?

Unlike generic linters that grep for style issues, it builds a call graph to pinpoint vibe-coding messes traditional tools miss, like AI-refactored duplicates or unreachable functions. The MCP server lets AI agents query code graphs instead of rescanning files, cutting token costs, while zero-config presets handle React, Django, and Axum entry points. Fast incremental scans and interactive REPL make code quality checks feel effortless.

Who should use this?

Backend devs shipping Python or Rust services via Cursor/Claude Code, where dead code piles up unnoticed. Frontend teams auditing JS/TS repos for Copilot-spawned clones and TODO cruft. Polyglot squads integrating into GitLab CI for pre-merge code quality metrics.

Verdict

Grab it via cargo install if AI drives your workflow—solid README and binaries across platforms make onboarding quick despite 18 stars and 1.0% credibility score. Early maturity means watch for edge cases in massive repos, but it's a smart code quality tool for the agentic era.

(198 words)

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