MCP vs LLM: What’s the Difference?
Jun 20, 2025
2 mins
Matt (Co-Founder and CEO)
TL;DR:
MCP and LLMs are complementary — not competing.
MCP is an infrastructure layer that manages how AI agents (like LLMs) securely access your app.
LLMs are cognitive engines — they interpret, generate, and decide. MCP ensures those agents act with the right identity, access, and control.
🤔 Why This Question Comes Up
As more apps integrate LLMs like GPT or Claude — especially as agents that interact with APIs — devs and product teams start asking:
“If I’m already using an LLM, do I need MCP too?”
“Aren’t they both ‘agent-related’ things?”
They are — but in completely different layers of the stack.
🔍 What Is an LLM?
A Large Language Model (LLM) is an AI system trained on massive datasets to understand and generate human-like text.
Examples:
OpenAI’s GPT-4
Anthropic’s Claude
Meta’s Llama
Google’s Gemini
LLMs are brains — not backends.
They:
Interpret instructions
Generate language
Perform reasoning and planning
Act as agents in other systems
But LLMs do not manage identity, access control, or governance.
🔐 What Is MCP?
Machine Client Protocol (MCP) is an emerging standard that governs how autonomous agents — like those powered by LLMs — access systems.
It’s not a model.
It’s not an AI.
It’s infrastructure.
MCP gives agents:
A verifiable identity
Delegated authority from users or systems
Scoped tokens for access
Audit trails of what they did
Think of it as the passport, visa, and customs check for agents before they call your API.
🧠 LLM + MCP: How They Work Together
Here’s a simple example:
Your customer uses a Claude-powered agent to summarize CRM data inside your SaaS app.
The LLM needs access to your platform’s API.
MCP handles:
“Which agent is this?”
“What data can it access?”
“Who delegated that access?”
“How long should this token last?”
“Can I revoke access later?”
Then the LLM agent uses that scoped, secure access to perform its task.
Without MCP:
The LLM might use an over-permissioned service account
You can’t trace or limit what it’s doing
You have no visibility into agent actions
Revoking or auditing access is painful (or impossible)
🧱 Analogy: Brain vs. Access Badge
LLM = Brain (it thinks, plans, generates responses)
MCP = Access Badge System (it checks who the agent is and what they’re allowed to do)
You wouldn’t let someone into your server room just because they’re smart.
They still need the right badge.
🚀 Why This Matters in AI-Native Apps
As LLMs move from chatbots to agents — actively calling APIs, triggering workflows, making decisions — MCP becomes critical:
✅ Secures API access from LLMs and other agents
✅ Supports safe delegation from human users
✅ Provides real-time audit and revocation
✅ Prevents privilege creep and token sprawl
👋 Summary: MCP vs LLM
🔐 Prefactor = Agent Access Infrastructure
If your app is being accessed by agents, you need more than auth —
you need agent identity, delegation, and audit.