MCP vs Function Calling: What's the Difference?

MCP vs Function Calling

If you've been following AI development, you've heard of both MCP (Model Context Protocol) and function calling. They sound similar — both let AI models use external tools — but they serve different purposes.

Function Calling

Function calling is a feature built into AI APIs (OpenAI, Anthropic, Google). You define functions in your API request, and the model can choose to call them.

Pros:

• Simple to implement
• Works within a single API call
• No extra infrastructure needed

Cons:

• Functions are defined per-request (not reusable)
• No standard format across providers
• Limited to the context of one conversation

MCP (Model Context Protocol)

MCP is an open standard for connecting AI assistants to external tools and data sources. Skills run as separate servers that any compatible AI client can connect to.

Pros:

• Reusable across different AI clients
• Standardized protocol (works with Claude, Cursor, etc.)
• Can maintain state between calls
• Community-driven ecosystem

Cons:

• Requires running a separate server process
• More setup than function calling

When to Use Each

Use function calling when:

• Building a custom AI application
• You need tight integration with your backend
• You want full control over the tool definitions

Use MCP when:

• You want to extend a desktop AI client (Claude, Cursor)
• You want to share tools with the community
• You need tools that work across multiple AI clients

The Future

MCP is gaining momentum as the standard for AI tool integration. As more clients adopt it, the ecosystem of reusable skills will only grow. AgentSkills is your home for discovering and sharing the best of them.