How MCP, RAG, Function Calling, Agent and Fine-tuning Reshape Future Applications
The latest developments in AI technology are reshaping enterprise applications. This article deeply analyzes how cutting-edge technologies such as MCP and RAG can help enterprises build AI solutions that adapt to their businesses.
Core content:
1. Technical definitions and core functions of MCP, RAG, Function Calling, Agent, and fine-tuning
2. Comparison of the advantages and collaborative applications of these technologies in different scenarios
3. Case analysis of scenarios such as intelligent legal assistants, predictive maintenance in manufacturing, and cross-border e-commerce operations
In 2025, AI technology is undergoing a profound transformation from "model worship" to "scenario adaptation". Enterprises are no longer satisfied with the "average capabilities" of general models, but are building "AI Lego" that adapts to their businesses through a combination of technologies such as MCP, RAG, Function Calling, Agent, and fine-tuning . How do these technologies define their boundaries? How do they create value together? This article will break down the three dimensions of definition and core functions, comparison of advantages and disadvantages, and scenario application, hoping to be helpful.
1. Technical Definition and Core Functions
MCP (Model Context Protocol): The “Universal Interface” of the AI Ecosystem
Definition: An open protocol proposed by Anthropic that standardizes the way large models interact with external tools and data, achieving "one-time development, universal use across all platforms".
Function:
① Dynamic tool discovery: AI models can call new tools without predefined functions.
②Cross-platform integration: unified docking with heterogeneous systems such as Slack and ERP.
③Permission isolation: Sensitive operations require secondary confirmation to ensure the security of enterprise data.
RAG (Retrieval-Augmented Generation): The “External Brain” of Large Models
Definition: Retrieve external knowledge through vector database to enhance the professionalism of large model answers.
Function:
① Dynamic knowledge update: Policy changes will take effect the next day.
② Enhanced explainability: The answer is marked with the source of the reference (such as Article X of the "XX Regulations").
③ Cold start friendly: Only a structured knowledge base is needed to cover 80% of basic scenarios.
Function Calling: The “Robotic Arm” of Large Models
Definition: Allows large models to call external APIs through JSON instructions, breaking through training data limitations.
Function:
① Real-time data acquisition: query of dynamic information such as weather and stock prices.
②System operation execution: control smart home and operate database.
③Complex task decomposition: Travel planning requires connecting weather, flight, and hotel APIs.
Agent: AI’s “autonomous driving mode”
Definition: Autonomous application system with memory, planning and tool use capabilities.
Function:
① Multi-step reasoning: Break down “marketing plan planning” into subtasks such as competitor analysis and budget allocation.
② Environmental perception: Dynamically adjust decisions based on enterprise data and business rules.
③Human-machine collaboration: AI handles structured tasks, while humans focus on fuzzy judgments.
Fine-tuning: the industry’s “custom tailor”
Definition: Adjust model parameters based on domain data to improve performance of specific tasks.
Function:
①Domain adaptation: The medical model learns CT image diagnosis rules.
② Cost optimization: The inference speed of the distilled small model is increased by 3-10 times.
③Privacy protection: localized deployment to avoid data leakage.
2. Comparison of technical advantages and disadvantages
3. Technology Collaboration Application Scenarios
Scenario 1: Intelligent Legal Assistant
Technology combination: RAG (legal document library) + Function Calling (contract comparison) ------ Agent (risk assessment)
Workflow:
1. RAG searches for the latest Civil Code provisions;
2. Function Calling calls the OCR interface to parse the contract;
3. Use prompt to generate an Agent to assess default risk and generate revision suggestions.
Scenario 2: Predictive maintenance in manufacturing
Technology combination: fine-tuning (equipment parameter model) + MCP (interconnection with ERP system) ------ Agent (fault diagnosis)
Workflow:
1. Fine-tune the model to learn relevant device data features;
2. MCP obtains production order data in real time;
3. Use prompt to generate Agent to predict faults based on equipment status and production schedule.
Scenario 3: Cross-border e-commerce operations
Technology combination: Function Calling (Exchange Rate API) + RAG (Tariff Policy Library) ------ Agent (Smart Product Selection)
Workflow:
1. Function Calling obtains real-time exchange rates and logistics prices;
2. RAG searches the import regulations of the target country;
3. Use prompt to generate an Agent to calculate the optimal pricing and logistics solution.
4. Enterprise Technology Selection Suggestions
Startups: Prioritize RAG+Function Calling and quickly verify scenarios at low cost (such as building a knowledge base with RAGFlow).
Medium and large enterprises: Focus on the MCP+Agent ecosystem to solve the problem of system silos (such as using MCP to connect to CRM/ERP).
Vertical fields: It is best to combine fine-tuning, as general models are difficult to adapt to industry specificity (such as medical imaging diagnosis).
Conclusion
When the technology dividend period ends, the competition in AI applications will return to its essence - who can combine technology modules more accurately to solve real business pain points.
