Function Calling vs. MCP: Why You Need Both—and How LangWatch Makes It Click

Confused by the growing buzz around MCP and Function Calling in the LLM tool ecosystem? You're not alone.

At first glance, they might seem like competing standards. But let’s set the record straight:

They’re not competing—they’re complementary.
And LangWatch helps you make the most of both.

Quick Primer: What’s Function Calling?

Function Calling lets an LLM decide when to use a tool and what parameters to send.

It’s great for:

1. Detecting when a tool should be invoked

2. Structuring tool inputs

3. Running tools inside a specific app

4. Letting you, the developer, handle the execution logic

Think of it like giving the LLM a remote control. It knows what button to press, but you still have to wire the device.

What is MCP? What does MCP stand for?

MCP (Model Control Protocol) picks up where Function Calling leaves off.

It solves:

1. How tools are exposed to LLMs across applications

2. Where tools live, how they're served, and how they’re discovered

3. Making tools reusable across systems—not just locked in a single app

4. Decoupling tool implementation from tool consumption

MCP is the infrastructure underneath tool usage. It’s less about the moment a tool is used, and more about creating a tool ecosystem that LLMs can plug into flexibly.

Function Calling: “I need to search the web now.”
MCP: “Here’s how web search is exposed, hosted, and can be reused anywhere.”

Why this matters for LangWatch users

At LangWatch, we’re building for teams who care about safe, reliable, and observable LLM pipelines. And that includes tool use.

Here’s how LangWatch fits into this picture:

• MCP-Enabled Monitoring: We support tracing across MCP-based tools. Whether you're hosting tools locally or integrating from a public tool registry, LangWatch can trace the LLM’s reasoning and tool execution across contexts.

• Function Call Observability: Our system automatically detects and logs function call events—parameters, tool latency, and results—giving you full visibility into LLM-tool interactions.

• Ecosystem Interoperability: As teams begin mixing tools from different providers (hosted via MCP), LangWatch ensures every step is tracked and validated—no matter where the tool comes from.

You don’t need to pick sides. Function Calling and MCP work best together.

And when you combine both with LangWatch, you don’t just use tools—you do it in a way that’s:

• Secure

• Observable

• Auditable

• Scalable

As AI systems scale and get more complex, this trifecta—Function Calling, MCP, and LangWatch—will be what separates spaghetti-code LLMs from truly robust AI infrastructure.

👉 Want to see how LangWatch traces function calls or MCP endpoints in action? Get started for free or book a demo and let’s walk through your stack together.