You're trying to help your friend move, so you bring your entire garage. Not just the dolly they need, but also your lawnmower, Christmas decorations, that broken exercise bike, and seventeen different screwdrivers.
Your friend stares at the chaos and asks, "Where's the dolly?" You point vaguely at the pile. "It's in there somewhere."
This is exactly what happens when you connect entire MCP servers to your AI instead of cherry-picking the tools you actually need.
The Great Tool Explosion
When MCP launched, everyone got drunk on the possibilities. "Look! I can connect my AI to everything at once!"
The result? AIs drowning in irrelevant tools, making bizarre choices, and burning through context windows faster than a startup burns through seed funding.
The Cursor Reality Check
Cursor's 40-tool limit makes this problem impossible to ignore.
A single MCP server can expose dozens of tools. Connect a few servers and boom—you're at your limit. Want to add that server with the one tool you actually need? Too bad. You're stuck choosing between your GitHub integration and your database tools like some sadistic Sophie's Choice for developers.
The Hidden Cost of Tool Hoarding
Even without limits, unused tools eat your context window for breakfast. Every tool gets loaded with descriptions, parameters, and examples. That weather API you never use? It's taking up tokens. Those dozen different ways to update a status when you only need one? Each costs context space.
You're paying for this inefficiency. Context tokens cost money, and you're literally paying to confuse your AI with irrelevant options.
It's like paying for a 50-room mansion when you only use the kitchen, except the unused rooms make it harder to find the kitchen.
The Choice Paralysis Problem
Giving an AI too many tools actively hurts performance. Ask your AI to "update the project status" when it has access to Linear, Notion, GitHub, Slack, and custom database tools, and watch it spend precious reasoning cycles trying to figure out what you meant.
There's a decent chance it'll pick the wrong one.
It's like having seventeen TV remotes scattered around your couch. Sure, they all control entertainment devices, but good luck finding the right one when you want to pause Netflix.
Storm MCP's Surgical Precision
This is why we built cherry-picking. Instead of connecting entire servers, select exactly the tools you need.
Working on a GitHub project? Pick just issue creation, pull requests, and code search.
Managing support? Choose ticket creation, messaging, and page creation.
Building data pipelines? Grab database queries, file uploads, and notifications.
Your AI goes from having dozens of random tools to having 3-6 laser-focused ones. It knows exactly what it can do, makes decisions faster, and actually does what you intended.
Real-World Impact
Let's say you're building an AI support agent. The naive approach might connect full servers for Zendesk, Slack, customer database, and email—easily 40+ tools.
But what does your support agent actually need? Look up customers, create tickets, send updates, send emails. That's 4 tools that do exactly what you need.
Your AI goes from being a confused Swiss Army knife to being a scalpel.
The Performance Gains
The difference is dramatic:
Before: Thousands of tokens for tool definitions, AI takes multiple reasoning steps, frequent wrong choices, higher costs.
After: Hundreds of tokens, immediate confident choices, near-zero mistakes, lower costs.
It's shopping at Costco for milk (overwhelming, expensive, you leave with stuff you didn't need) versus a corner store (focused, efficient, you get exactly what you came for).
The Maintenance Bonus
Cherry-picking makes maintenance infinitely easier. When you connect entire servers, you're at the mercy of every update. New tools appear in your context, deprecated ones confuse your AI, refactored descriptions change behavior overnight.
With cherry-picked tools, you have granular control. Update when you want, test incrementally, never get surprised by someone else's "helpful" additions.
The Future is Surgical
The early MCP days were about proving the concept—showing AI could connect to external tools at all. We dumped everything into context and marveled that it worked.
Now we're optimizing. The future belongs to teams that precisely craft their AI's capabilities, not those who throw everything at the wall.
Your AI doesn't need to be a generalist that kinda-sorta does everything. It needs to be a specialist that does exactly what you need, perfectly, every time.
Ready to give your AI surgical precision instead of a rusty toolshed? Explore curated, cherry-pickable tools at Storm MCP—where less is definitely more.