PromptForward

•4 min read•By PromptForward Team

MCP Overload: Why Your LLM Agent Doesn’t Need 20 Tools

MCP is powerful — but with great power comes great token bloat.

MCP Overload: Why Your LLM Agent Doesn't Need 20 Tools

MCP — the Model Context Protocol — promised a lot. A unified standard for tool use across AI agents. A way to wire up everything: web search, database access, APIs, file systems, you name it.

Just connect your agent to a few dozen tools, and voilà — superintelligence!

But here’s what many developers are discovering the hard way:

The more tools you give an agent, the worse it performs.

Slower. Dumber. More expensive. In this post, I’ll walk through why MCP overload is a real problem — and what better agent design looks like in the real world.

1. More Tools, More Tokens, More Problems

Each MCP tool comes with a schema — a description of what it does and how it should be used. These schemas are injected into the system prompt. Add 10 or 20 tools, and suddenly your prompt is bloated with several thousand extra tokens.

That means:

  • Slower response times
  • Increased latency
  • Reduced context for your actual task
  • Higher token costs

As one developer (@thdxr) put it on X:

“It’s funny how many people wrote huge predictions for MCP without checking how LLM performance degrades when you add even 10 tools.”

2. Your Agent Becomes Dumber, Not Smarter

Too many tools leads to cognitive overload — not for you, but for the model.

Instead of focusing on the user prompt, the model is now juggling a dozen similar options: "Should I use edit_tool_v1, edit_tool_v2, or replace_line_with_regex?"

One engineer watched their agent try 18 different edit-related tools before finally giving up and writing a shell command:

sed -i 's/foo/bar/g' file.txt

If that’s the fallback, why not just do it first?

3. Your Bills Start Creeping Up

All that tool overhead? You’re paying for it — literally.

Longer prompts, more calls, and more steps all mean higher API costs. One developer described their experiment with a large MCP agent like this:

“It worked… but the performance/price cost was crazy.”

You’re not just slowing down. You’re spending more for worse results.

4. Bigger Prompts, Weaker Alignment

There’s another cost: accuracy.

The more tools you cram into the system prompt, the harder it is to steer the model toward the actual task. The LLM starts focusing on tool selection logic instead of the user’s intent.

“People who don’t build with AI don’t realize how hard it is to steer the model when your system prompt is 90% tool definitions.” — @prashant_hq

If your agent stops following directions, it might not be the model’s fault. It’s your toolset’s.

5. This Isn’t How Developers Use Tools

When you want to rename a file, you don’t open 5 apps. You type: mv file1.txt file2.txt.

LLMs can do the same — if you let them.

Some engineers are ditching heavy MCP setups entirely and just telling their models to use bash or Python:

import os  
os.rename("file1.txt", "file2.txt")

It’s faster, simpler, and far more natural for both developers and LLMs.

6. So What Should You Do Instead?

Here’s what experienced builders are doing:

  • Give the model 1–5 well-scoped tools
  • Let it use general-purpose interfaces (e.g., shell, Python eval)
  • Use dynamic tool loading based on the current task
  • Avoid overlapping or redundant tool functions
  • Keep your system prompt small and focused

“Moving from MCPs to libraries and giving the LLM a simple eval() tool solves so many of these issues.” — @ProgramWithAI

Let the model write code instead of playing multiple-choice.

7. Agents Should Be Reliable, Not Overengineered

At the end of the day, what matters isn’t how many tools your agent could use. It’s whether it actually works — under real-world input, with real-world constraints.

That’s why prompt testing platforms like PromptForward exist: to help teams version, test, and deploy prompts like code — with real datasets, not just vibes.

You’re not trying to win a benchmark. You’re trying not to break production.

TL;DR

MCP is powerful — but with great power comes great token bloat.

If you’re building LLM agents, keep your tools focused. Start small. Favor code execution or shell access over schema overload. And test your prompt behavior before your users do.

Because in the end, an agent with 4 good tools will outperform one with 40 bad ones — every time.