MCP vs Function Calling: When to Use Each

“Should I use function calling or MCP?” It’s a question I see constantly now that both approaches have matured. And honestly, the confusion is understandable—they both enable AI models to use external tools, and the terminology overlaps in confusing ways.

Here’s the short answer: they’re not really competitors. They solve related but different problems, and in many cases, you’ll use them together. But understanding when to reach for which—and why—matters a lot for building effective AI applications.

This guide breaks down both approaches, compares them honestly, and gives you a framework for deciding which to use. By the end, you’ll know exactly how to think about function calling versus MCP for your projects.

What Is Function Calling?

Function calling (sometimes called “tool use”) is a capability built into large language models that lets them request to call external functions based on conversation context.

Here’s how it works:

You define available functions - You tell the AI what functions exist, their names, descriptions, and parameter schemas
The AI decides when to use them - Based on conversation, the model decides if a function call would help
The AI specifies the call - It returns a structured JSON object with the function name and arguments
Your code executes it - Your application actually runs the function and returns results to the AI
The AI incorporates the result - The model uses the function output to continue the conversation

OpenAI popularized this approach with GPT-3.5 and GPT-4 in 2023. Here’s what a typical function calling integration looks like with the OpenAI API:

import openai

# Define available functions
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string", "description": "City name"},
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
                },
                "required": ["location"]
            }
        }
    }
]

# Make the API call
response = openai.chat.completions.create(
    model="gpt-5",
    messages=[{"role": "user", "content": "What's the weather in Tokyo?"}],
    tools=tools
)

# Handle function call if requested
if response.choices[0].message.tool_calls:
    function_call = response.choices[0].message.tool_calls[0]
    # Your code executes the actual function here

Every major AI provider now supports function calling:

OpenAI - The original implementation with GPT-5
Anthropic - “Tool use” in Claude 4
Google - Function calling in Gemini 3

The implementations are similar but not identical. Each has its own JSON schema format, API structure, and nuances.

For more on using these APIs, check our OpenAI API tutorial or Claude API tutorial.

What Is MCP?

MCP—Model Context Protocol—takes a different approach. Instead of being a feature within one AI provider’s API, it’s a standardized protocol for connecting AI applications to external tools and data sources.

I’ve written a complete guide on what MCP is and how it works, but here’s the quick version:

MCP defines a standard way for:

Servers to expose tools, resources, and prompts
Clients (AI applications) to discover and use those capabilities
Communication using JSON-RPC 2.0

The key insight is that MCP creates a layer of abstraction. An MCP server works with any MCP-compatible client—whether that’s Claude Desktop, ChatGPT, Gemini, or a custom application.

┌─────────────────┐     ┌─────────────────┐
│   Claude        │ MCP │  Filesystem     │
│   Desktop       │────▶│  Server         │
└─────────────────┘     └─────────────────┘
        │                       △
        │ MCP                   │
        ▼                       │
┌─────────────────┐             │
│   ChatGPT       │ MCP         │
│   Desktop       │─────────────┘
└─────────────────┘

Same server, multiple clients. That’s the MCP advantage.

The Key Differences

Let’s compare these approaches directly. Understanding these differences will clarify which to use when.

Vendor Specificity

Function calling is vendor-specific. OpenAI’s function calling works with OpenAI’s API. Anthropic’s tool use works with Anthropic’s API. The formats are similar but not interchangeable.

MCP is vendor-agnostic. It’s an open standard now governed by the Linux Foundation. Build an MCP server once, and it works with any MCP-compatible client.

This matters a lot for portability. If you build tools for OpenAI function calling, switching to Claude means rewriting your integration. If you build MCP servers, you can switch clients without touching your tools.

Statefulness

Function calling is primarily stateless. Each API call is independent. You define functions, the AI calls them, you return results. The AI has no persistent connection to your function implementation.

MCP supports stateful, persistent connections. An MCP server can maintain context across multiple calls, remember previous interactions, and manage ongoing sessions. This enables more complex, longer-running workflows.

Scope and Complexity

Function calling is optimized for simple, discrete actions. “Get the weather.” “Send an email.” “Query this database.” One function, one result, done.

MCP handles more complex scenarios. A single MCP server can expose multiple tools, resources (data the AI can read), and prompts (reusable templates). It’s designed for richer, ongoing interactions.

Execution Responsibility

Function calling puts execution in your application code. The AI says “I want to call this function with these arguments,” but your code actually runs it.

MCP encapsulates execution in the server. The server handles everything—the AI just sends MCP requests and gets responses. This separation makes servers reusable across applications.

Standardization

Function calling follows each vendor’s API design. There’s no industry standard—each provider made their own choices.

MCP is a formal specification with versioning, governance, and industry-wide adoption. This standardization means better tooling, documentation, and ecosystem support.

Summary Table

Aspect	Function Calling	MCP
Vendor	Provider-specific	Open standard
State	Stateless	Supports stateful sessions
Scope	Simple, discrete actions	Complex, multi-capability servers
Execution	Your application executes	Server handles execution
Portability	Vendor lock-in	Cross-platform
Learning curve	Lower	Higher
Rich ecosystem	Limited	Growing rapidly

When to Use Function Calling

Function calling is the right choice in specific scenarios. Here’s when it shines:

Quick Prototypes and MVPs

When you’re building something fast and don’t know yet if it’ll work, function calling gets you there faster. No server infrastructure to set up—just define functions in your API calls and handle the responses.

If I’m testing whether an AI can help with a particular task, function calling lets me iterate in minutes, not hours.

Single-Vendor Applications

If your entire application is built around one AI provider—you’re all-in on OpenAI, or you’re building exclusively for Claude—function calling is simpler. You’re already deep in that API; just use its native capabilities.

The portability advantage of MCP doesn’t matter if you’re never switching.

Simple, Stateless Tools

For straightforward actions that don’t need persistent context—check the weather, calculate something, look up a value—function calling is perfectly adequate. You don’t need the additional complexity of MCP for a one-and-done function.

Tight Latency Requirements

Direct function calling can be marginally faster than going through an MCP server layer. For applications where every millisecond counts, reducing indirection helps.

Examples

A chatbot that fetches product availability from your inventory system
An AI assistant that can send emails or calendar invites
A writing tool that checks grammar using an external service
Quick internal tools that only your team uses

When to Use MCP

MCP makes more sense in other scenarios. Here’s when to reach for it:

Multi-Platform Applications

If your tools need to work with Claude, ChatGPT, Gemini, and custom applications, MCP saves you from maintaining multiple integrations. Build the MCP server once; connect it from anywhere.

Complex, Stateful Workflows

When your AI needs to maintain context across interactions—remembering what happened earlier, tracking state, managing sessions—MCP’s design handles this elegantly. The server maintains state; the AI just uses it.

Enterprise and Team Scenarios

For tools that multiple developers or applications will use, MCP’s server-based architecture makes sense. Deploy the server once, and everyone connects to it. Updates and maintenance happen in one place.

Building Reusable Infrastructure

If you’re creating tools that should outlive any particular AI model or provider, MCP’s standardization protects your investment. You’re building to a standard, not to a vendor’s current API.

Rich Capability Servers

When you want to expose multiple tools, resources, and prompts from a single integration, MCP’s primitives give you more flexibility than function calling’s function-list approach.

Examples

A database server that any team’s AI tools can query
An internal knowledge base accessible from multiple AI clients
Development tools that work with various AI-powered IDEs
Customer service infrastructure shared across channels

For hands-on guidance, check our MCP server Python tutorial.

Using Both Together

Here’s what many people miss: function calling and MCP are complementary, not competing. You can use both in the same system to get the best of each approach.

The Bridging Pattern

A powerful pattern is using function calling to invoke MCP servers. You define a generic function that routes calls through MCP:

# Define an MCP-bridging function for use with function calling
tools = [
    {
        "type": "function",
        "function": {
            "name": "call_mcp_tool",
            "description": "Call a tool on an MCP server",
            "parameters": {
                "type": "object",
                "properties": {
                    "server": {"type": "string", "description": "MCP server name"},
                    "tool": {"type": "string", "description": "Tool to invoke"},
                    "args": {"type": "object", "description": "Tool arguments"}
                },
                "required": ["server", "tool"]
            }
        }
    }
]

When the AI calls this function, your application:

Identifies the target MCP server
Invokes the specified tool with the given arguments
Returns the MCP response to the AI

This pattern gives you:

Vendor flexibility in which AI you use (OpenAI, Anthropic, etc.)
Tool portability through the MCP ecosystem
Centralized tool management via MCP servers

Architecture for Combined Use

┌────────────────────────────────────────────────────────────┐
│                    Your Application                        │
├────────────────────────────────────────────────────────────┤
│                                                            │
│   ┌───────────────────┐    ┌───────────────────┐          │
│   │    OpenAI API     │    │   Anthropic API   │          │
│   │  (function call)  │    │   (tool use)      │          │
│   └─────────┬─────────┘    └─────────┬─────────┘          │
│             │                        │                     │
│             ▼                        ▼                     │
│   ┌──────────────────────────────────────────┐            │
│   │           MCP Client Layer               │            │
│   │    (Routes calls to MCP servers)         │            │
│   └──────────────────┬───────────────────────┘            │
│                      │                                     │
└──────────────────────┼─────────────────────────────────────┘
                       │
          ┌────────────┼────────────┐
          ▼            ▼            ▼
    ┌──────────┐ ┌──────────┐ ┌──────────┐
    │ Database │ │   Git    │ │  Slack   │
    │  Server  │ │  Server  │ │  Server  │
    └──────────┘ └──────────┘ └──────────┘

This architecture means you can:

Swap AI providers without touching MCP servers
Add new MCP servers without changing AI integration
Test with different models while using the same tools

When This Makes Sense

The bridging pattern works well when:

You’re building a sophisticated AI application
You want maximum flexibility for future changes
You have multiple MCP servers to integrate
Your team is already investing in MCP infrastructure

For simpler applications, this might be overengineering. Use judgment.

Making the Decision: A Framework

Still not sure which to use? Here’s a decision framework:

Ask These Questions

How many AI providers do I need to support?

One: Function calling is probably fine
Multiple: MCP provides portability

How complex are my tools?

Simple, discrete actions: Function calling works well
Rich, multi-capability systems: MCP handles this better

Do I need persistent state?

No: Either approach works
Yes: MCP’s stateful sessions help

Am I building for just myself or for a team?

Solo project: Keep it simple (function calling)
Team infrastructure: MCP’s reusability shines

What’s my timeline?

Quick prototype: Function calling is faster to start
Long-term product: MCP’s investment pays off

The Decision Tree

Start
  │
  ▼
Need multiple AI providers? ──Yes──▶ Use MCP (or bridge)
  │
  No
  │
  ▼
Need stateful, complex tools? ──Yes──▶ Consider MCP
  │
  No
  │
  ▼
Building team infrastructure? ──Yes──▶ MCP for reusability
  │
  No
  │  
  ▼
Use function calling (simpler for basic use cases)

My General Recommendation

For most developers starting out with AI tool use:

Learn function calling first - It’s simpler and gets you building quickly
Move to MCP when complexity grows - When you need persistence, multiple clients, or team sharing
Consider bridging for sophisticated systems - When you want both flexibility and simplicity

There’s no wrong answer. Both approaches work, and the AI ecosystem supports both.

Frequently Asked Questions

Can I use MCP with OpenAI?

Yes. OpenAI announced MCP support in March 2025, and ChatGPT Desktop supports MCP servers directly. You can also bridge MCP into function calls for API-level integration.

Is function calling being deprecated?

No. Function calling remains actively developed by all major providers. It’s complementary to MCP, not replaced by it.

Which is faster?

Direct function calling has slightly lower latency since there’s no MCP server layer. For most applications, the difference is negligible. If latency is critical, benchmark your specific use case.

Can MCP servers call functions on other MCP servers?

MCP servers typically operate independently, but you can build orchestration layers that coordinate multiple servers. The protocol itself doesn’t prevent chaining.

Do I need to write my own MCP servers?

Not necessarily. There are thousands of community-built MCP servers for common integrations—databases, file systems, APIs, productivity tools. Start with existing servers and build custom ones only when needed.

Making AI Actually Useful

At the end of the day, both function calling and MCP exist for the same purpose: making AI models useful beyond generating text. They’re about connecting intelligence to action.

Function calling got us started. MCP is standardizing and scaling the approach. Together, they’re enabling a new generation of AI applications that don’t just talk—they do.

My advice? Start building. Pick whichever approach matches your current needs, and don’t overthink it. You can always refactor later as your requirements evolve.

The best way to understand these tools is to use them. Go build something, and see which approach feels right for your work.