Say Goodbye to Outdated Dependencies

Search for a command to run...

No comments yet. Be the first to comment.
TL;DR: As most new code is now generated by LLMs, Code Feedback MCP provides the critical feedback loop that enables AI to automatically validate, fix, and improve its own code generation in real-time. It's the missing piece that transforms unreliabl...
Your team has 14 versions of the same code review skill across 5 repos, with no way to sync them. when one improves, the others don't. drop a folder in .claude/skills/ and pray nothing drifts. source

AI agents are unreasonably good at writing SQL. They get specific error messages, fix their own mistakes in one retry, and can introspect query performance with EXPLAIN ANALYZE before you even ask. No

n8n has over 400 integrations. Zapier claims 7,000+. Every single one was hand-built, tested against a moving API, and will eventually break when that API ships a v2. The entire workflow automation in

Part of the "Your Next Startup" series, where I break down startup ideas I think are worth building. Auth0 sold for \(6.5B. Okta is worth \)15B+. CyberArk, Delinea, BeyondTrust, all printing money fro

For as long as software has existed, we've been building two doors into our systems. Door one: the UI, a carefully designed surface where humans point, click, and occasionally rage-quit. Door two: the

One of the most frustrating things when building with AI coding assistants is when they happily write code for you… but the dependencies they suggest are outdated or simply wrong. You know the drill:
You ask your LLM to add a library.
It writes npm install some-package@1.0.0… but the latest version is actually 3.2.4.
Or worse, it invents a version that doesn’t even exist.
Suddenly, instead of building features, you’re wrestling with dependency mismatches.
I built the Dependency MCP Server to solve exactly this problem.
The Dependency MCP is an MCP (Model Context Protocol) server that lets your AI development tools check dependencies across multiple registries in real time.
That means whenever the AI suggests a package, it can instantly verify:
✅ What the latest version is
✅ Whether a specific version exists
✅ Full package metadata including all available versions
✅ Run bulk checks across your entire dependency list
It supports all the major registries: npm, PyPI, Maven, NuGet, RubyGems, Crates.io, and Go modules.
So whether you’re in Node.js, Python, Java, .NET, Ruby, Rust, or Go, the AI never needs to guess again.

This isn’t just about convenience. Correct dependencies make a huge difference in:
Reducing errors – no more wasted time debugging phantom versions.
Faster development – the AI can give you working install commands immediately.
CI/CD reliability – bulk validation tools let you enforce correct versions across pipelines.
Security audits – you can fetch full package metadata for reviews.
Instead of trusting the model’s memory (which is always a little stale), you give it a direct way to ask the source of truth: the package registries themselves.
One of the coolest things about building MCP servers is that you can actually use Cursor’s own MCP integration to create an automatic development feedback loop.
The flow looks like this: you start by writing or updating your MCP server, then register it in Cursor’s configuration. Once it’s wired up, Cursor can immediately call your server and test each of the tools you’ve exposed. If something doesn’t work, Cursor surfaces the error right away - and even suggests fixes or improvements directly in your editor. That means you’re not just coding; you’re effectively building software that validates itself, with an AI co-pilot constantly reviewing and stress-testing your MCP server in real time.

Wire it into your AI tooling (like Claude Desktop or Cursor), just drop this into your config:
{
"mcpServers": {
"dependency-checker": {
"command": "npx",
"args": ["dependency-mcp"]
}
}
}
Building with AI is amazing - but only if the code it writes actually runs. With the Dependency MCP server, you never have to worry about outdated or invalid dependencies again.
For me, this has completely changed the dev cycle: instead of correcting the AI, the AI corrects itself.