What is the difference between MCP and Function Calling? How is it related to AI Agent?

Building in today AI During the application process,Function Calling,MCP as well as AI Agent These are three closely related but distinct concepts. Understanding their differences and connections is important for developers to design appropriate AI The system is crucial. We can compare these three to " call instructions → scheduling system → autonomous executor ", solving problems at different levels.

Level 1: Function Calling - Make the model "able to call tools"

Function Calling This is the most basic layer, and its core goal is to enable large models to correctly generate instructions for calling external functions . Developers only need to define the function interface, and the model will know which functions can be called through prompts, and select appropriate functions and parameters during reasoning.

Example :

{ "function": "getWeather", "parameters": { "city": "Beijing" }}

The model can call weather in this way API And returns the current weather information.

Applicable scenarios :

• Check the weather
• Check stock prices
• Simple database query

limitation :

• Once the number of tools increases (for example, dozens or hundreds), it becomes difficult for the model to accurately select from a long list of functions. The prompt words become complex, the context expands rapidly, and the effect is greatly reduced.

Level 2: MCP (Multi-tool Calling Protocol) - Efficient access to a large number of tools

As the number of tools increases,Function Calling Obviously, it is not enough. At this time, we need to introduce MCP, which is the multi-tool calling protocol.MCP Provide a unified standard tool access mechanism, just like a AI Sockets allow all tools to connect using standardized protocols. The model no longer directly faces all function details, but instead MCP Access various "tool services" and request them dynamically as needed.

Reference: [Reprinted] MCP (Model Context Protocol) Comprehensive Research Report: Concepts, Practices and Future Trends

Example scenario : Let Agent Query CRM middle Acme The company's sales contracts last quarter PDF, send an email, and then schedule a calendar meeting.

use Function Calling, you need to define:

• Query CRM function
• Functions for search results files
• Function to send email
• Calendar management functions

Each function must be given a prompt word, and the model must be carefully selected each time, which is very inefficient.

If MCP is used, the model only needs to know which services are connected (such as CRM service, email service, calendar service), and the specific call details are handled by the MCP protocol, which greatly simplifies the context and selection complexity and improves scalability and efficiency.

Advantages :

• Supporting access to standardized tools
• Dynamically discover and call services
• Improve calling efficiency in multi-tool environment

Limitations :

• The ecosystem is not yet mature, and third-party tool support is limited
• Performance bottlenecks under large-scale concurrency remain to be verified
• The support of mainstream large models (such as OpenAI) is not high, and Anthropic is currently leading the way

Level 3: AI Agent — autonomously completes complex tasks

The next level up is AI Agent, which is not just a "calling tool" but has a certain degree of autonomy and can perform closed-loop operations of planning, decision-making, and execution.

Features :

• Have a sense of purpose: understand the mission objectives
• Possess planning ability: can break down tasks into multiple steps
• Possess memory or context tracking capabilities: can optimize subsequent actions based on past actions

Example task : Automatically make travel plans → Book air tickets → Arrange accommodation → Send emails → Generate itinerary

This complex process, if used Function Calling or MCP To do it, it still needs human arrangement . Agent You can plan the steps yourself and call the appropriate tools to complete the entire process.

Advantages :

• Strong autonomy
• Multi-step tasks are more natural
• A closer experience to a “human-like assistant”

challenge :

• High development cost and difficult debugging
• Model behavior is unpredictable and prone to deviation
• Security and stability are difficult to guarantee

? AI intelligent system three-layer architecture diagram

+--------------------------------------------------------------+| Level 3: AI Agent ||--------------------------------------------------------------|| ✅ Autonomous task planning || ✅ Multi-round decision-making capability || ✅ Autonomous call of tools (via MCP) || || ? Examples: travel planning, project management, automatic reply to emails, etc. |+--------------------------------------------------------------+ ▲ │+--------------------------------------------------------------+| Level 2: MCP ||--------------------------------------------------------------|| ✅ Unified access standard for multiple tools || ✅ Reduce the burden of context || ✅ Dynamically select service endpoints (such as CRM, email, calendar, etc.) || || ? Examples: unified access to enterprise internal APIs, databases, file management, automation services, etc. |+--------------------------------------------------------------+ ▲ │+--------------------------------------------------------------+| Level 1: Function Calling ||--------------------------------------------------------------|| ✅ Model's ability to call external functions (via structured JSON) || ✅ Good for simple, well-defined tasks || || ? Examples: checking the weather, checking exchange rates, performing a search query, etc. |+--------------------------------------------------------------+

✅ Comparison summary of the three

Conclusion

Function Calling It is the basic ability .MCP is the system bus , and AI Agent is the intelligent brain . The three are not in opposition, but progressive and collaborative to build a powerful AI System. Future AI Applications may combine the advantages of the three to realize an intelligent system that can "think, call, and execute ".