Description
Description:
Hi community,
I’m trying to deeply understand the distinction between OpenAI’s Function Calling and Model Context Protocol (MCP). While both involve structured data (JSON) interaction with the model, their purposes and workflows seem fundamentally different. Here’s my current understanding:
Function Calling
The model decides when to invoke external tools/APIs based on user input.
Output: Structured requests (e.g., {"function": "get_weather", "location": "Tokyo"}).
Use case: Dynamic action execution (e.g., calling an API, querying a database).
Model Context Protocol (MCP)
External systems inject structured context (e.g., session state, retrieved knowledge) into the model’s prompt.
Input: Predefined JSON format describing context (e.g., {"user_preferences": {"theme": "dark"}}).
Use case: Context-aware responses without model-initiated actions.
Key Confusion:
Is MCP more about passive context enrichment, while Function Calling is about active model-driven actions?
How do they complement each other in complex workflows (e.g., a chatbot using both context and API calls)?
Would love insights from anyone who has implemented both! Examples or architecture diagrams would be especially helpful.
Tags: #function-calling #mcp #structured-data #integration