Function Calling vs. MCP: LLM Integration Strategies

Function Calling: Translates natural language into structured JSON function calls, enabling LLMs to choose and execute specific functions with defined parameters, suitable for well-defined tasks in controlled environments where the application owns the execution logic. For example, converting "What’s the weather in Paris?" into a `get_weather` function call with "Paris" as the parameter.
MCP (Model Communication Protocol): Standardizes communication between AI models and external tools/APIs using a universal protocol (e.g., JSON-RPC), eliminating the need for custom connectors and facilitating real-time data access with enhanced security, crucial for interoperability across vendors and scalable, maintainable, and compliant systems.
Key Difference: Function calling focuses on decision-making for task execution based on natural language input, while MCP standardizes system-level integration and communication between models and external resources.
Use Case Consideration: Function calling is best when converting natural language into structured actions for simple, well-defined tasks, whereas MCP is preferred for integrating with numerous tools/systems requiring interoperability, scalability, and compliance.
RAG Context (According to Eduard Dolynskyi): In Retrieval-Augmented Generation (RAG) setups, the "execution outside the model" aspect of function calling may be less distinct, as the model often becomes integrated into the execution loop.
Alternative Approach (According to Oriol Jaumà Lara): Code-based agents are an alternative to function calling with JSON, with pipelines that are not significantly different from MCP.

Source:

https://www.linkedin.com/posts/eordax_ai-llm-functioncalling-activity-7326267562722246657-RZDe