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What is the difference between Model Context Protocol (MCP) and traditional Function Calling? How to choose?

Written by
Audrey Miles
Updated on:July-10th-2025
Recommendation

Model Context Protocol (MCP) and traditional Function Calling, technical comparison and in-depth analysis of application scenarios.

Core content:
1. Comparison of the core differences between MCP and Function Calling
2. Analysis of typical application scenarios of MCP and Function Calling
3. Comparison of technical implementations and prediction of future evolution trends

Yang Fangxian
Founder of 53AI/Most Valuable Expert of Tencent Cloud (TVP)

1. Core Difference Comparison Table



2. Typical Application Scenarios

1.  Prefer MCP scenarios


-  **Cross-platform tool integration**    For example: Let Claude Desktop access local files + Jira API + private database at the same time
-  **Sensitive Data Isolation**    Access regulated data such as medical/financial data through MCP Server proxy to avoid direct exposure to LLM
-  **Dynamic context management**    Need to automatically switch between different data sources based on the conversation state (such as switching GitHub repository context)
-  **Multiple LLM Supplier Switching**    Unified resource interface, no need to rewrite tool call logic for each LLM

2.  Prioritize Function Calling scenarios

-  **Rapid Prototyping**    Simple weather query/calculator and other single function extensions
-  **Closed Environment Applications**    The tool logic is completely built into the current application (such as internal knowledge base Q&A)
-  **Lightweight interaction**    Scenarios that do not require complex permission control or resource discovery
-  **Vendor lock-in scenario**    Deep reliance on specialized features of a specific LLM (such as GPT-4)

3. Technical Implementation Comparison

IV. Evolution Trend Prediction



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