LLM Function Calling vs MCP

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[MCPhubs]

When I first heard of MCP (Model Context Protocol), I thought: “Isn’t this just LLM function calling with extra steps?”

But after exploring it—and submitting my first MCP server to MCPHubs—I realized MCP is tackling a different, much bigger problem.

🔧 What is LLM Function Calling?
LLM function calling is great for internal model logic. You give the model a spec of callable functions, and it returns structured JSON to run them.
It’s powerful, but it’s also limited to a single model's session.

Use cases:

Structured reasoning within one request

Calling predefined functions in your backend

Reducing hallucinations for expected tasks

🌐 What is MCP?
MCP is a network protocol. It lets you expose real-world capabilities—search, APIs, tools—so AI agents across systems can discover and use them.

You publish a YAML file at /.well-known/mcp.yml, define your actions and endpoints, and you're in. No SDK, no lock-in.

✅ Why I Found MCP Useful (via MCPHubs)
I submitted a service to MCPHubs, the community directory for MCP services. Within minutes, my endpoint was:

Discoverable by other agents

Composable across systems

Reusable without re-prompting or custom logic

It felt like turning my isolated API into something that could be part of an AI-native web.

🔁 Summary
Feature LLM Function Calling MCP Protocol
Scope In-model logic Cross-agent, cross-service logic
Discoverability Manual Automatic via /.well-known
Reusability Per prompt / session Long-term, declarative
Ecosystem Fit Model-specific Agent-agnostic

LLM function calling is like adding functions to the model.
MCP is like adding your service to the internet of AI.

🧠 If you’re thinking long-term and want your tools to live beyond one model, MCP is worth a look.

Let me know if you're experimenting with MCP or have listed your service—I’d love to see what you're building!