MCP vs Function Calling, which one should you choose?

     However, this integration is not easy. The output of LLMs is usually random and unpredictable. How to effectively control and structure it while meeting business needs has become one of the biggest challenges faced by enterprises in actual deployment.

     As technology develops, two mainstream solutions have gradually emerged: Function-Calling and Model Context Protocol (MCP). Although both methods aim to improve the predictability and production readiness of LLMs, they have significant differences in design concepts, implementation methods, and applicable scenarios. A deep understanding of these differences will not only help companies make wise technical choices when integrating LLMs, but also lay the foundation for building more efficient and reliable intelligent systems.

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How to understand  Function Calling  ?

     As a more direct mechanism, usually embedded in a large language model (LLM), Function Calling is used to dynamically call external functions or APIs when the model generates a response. It mainly involves the following components:

• User: Initiate a query.

• Large Language Model (LLM): directly parses the query, decides whether a function needs to be called, and generates a response.

• Function declaration: predefined external function interface (such as how to call the weather API).

• External API: provides specific data or services.

{ "type": "function", "function": { "name": "get_weather", "description": "Get the current weather information for the specified location", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "City name, for example: Hong Kong, Taipei" }, "unit": { "type": "string", "enum": ["celsius", "fahrenheit"], "description": "Temperature unit" } }, "required": ["location"] } }}

How to understand  Model Context Protocol (MCP)  ?

     Although Function-Calling performs well in handling structured tasks, the Model Context Protocol (MCP) takes a completely different design approach.

     As a hierarchical technology, MCP provides a more flexible and controllable way of interaction for large language models (LLMs) by systematically organizing context and prompts. Compared with the rigid constraints of function calls, MCP is better at handling complex, multi-step dialogue scenarios, especially in scenarios that need to maintain contextual coherence and dynamically adapt to user needs.

     Generally speaking, the design of MCP is more inclined to modular and distributed systems, emphasizing clear process control and intermediate state management. It mainly involves the following core components:

• User: Initiates a query (e.g. "What's the weather in Hong Kong?").

• MCP Client: Receives user queries, coordinates tool selection and task allocation.

• MCP Server: Executes specific tool calls (such as calling the weather API).

• LLM (Large Language Model): Processes natural language and generates final output.

• Tools: External APIs or other functional modules (such as weather API).

     The following is a simple server example implemented using the MCP framework to obtain weather information for a specified location. The specific code can be found below:     

from  mcp  import  MCPServer, Tool, Parameter
# Initialize the MCP serverserver = MCPServer()
@server.toolclass  WeatherTool ( Tool ):    """A tool for obtaining weather information for a specified location"""
    @server.function    def  get_weather ( self, location: Parameter( description = "city name" ),                    unit: Parameter( description= "Temperature unit" , default= "celsius" )):        """Get the current weather at the specified location"""        # Implementation of calling weather API (simulated data here)        return  { "temperature" :  22 ,  "condition" :  "sunny" ,  "humidity" :  45 }
# Start the serverserver.start()

     In actual scenarios, the core of MCP is to decompose and organize the interaction process in a hierarchical manner. Each layer of context provides specific instructions, constraints or background information for LLM, so that when the model generates a response, it can maintain its creativity and ensure that the output is highly consistent with business goals.

     Specifically, each layer of MCP may contain different information modules, such as task objectives, user background, business rules, or historical conversation records. When generating a response, the model will comprehensively consider the information of all contextual layers to ensure the accuracy and relevance of the output. This layered design not only provides clear guidance for the model's behavior, but also avoids overly restricting its generation capabilities, allowing LLM to demonstrate higher flexibility and intelligence in complex scenarios.

Analysis of the differences between MCP  and  Function Calling  design concepts

     1. MCP design concept: modular, distributed and controllable intelligent task execution framework

• Modular and distributed architecture: MCP divides tasks into multiple independent modules (such as Client, Server, LLM, Tools), each of which focuses on specific functions. This design approach is very suitable for distributed systems and can support the collaborative work of multiple components to ensure efficient completion of tasks.

• Intermediate state management: MCP implements clear state management in each processing step (such as tool selection, API call, data processing). This management method helps to locate problems during debugging and enables effective error handling.

• Security and Control: MCP introduces security control mechanisms such as "API request approval" to enhance the security and controllability of the system, making MCP particularly suitable for application scenarios that require strict permission management and high security requirements.

    2. Function Calling design concept: integrated, model-driven and lightweight function expansion solution

• Integration and efficiency: Function Calling embeds the function calling logic directly into the LLM, thus simplifying the system architecture and reducing the middle layer. This design helps to improve the system's response speed and is suitable for simple tasks that require fast response and efficient execution.

• Model-driven: In Function Calling, LLM plays a core role and is responsible for the entire process from parsing user queries to generating responses. The design relies on the intelligence of a large language model to understand function declarations and provide precise function calls based on them.

• Lightweight architecture: By removing the complex middle layer, Function Calling is more lightweight and suitable for embedded systems or single applications. It can reduce system complexity and improve maintenance efficiency.

MCP vs Function Calling, which one should you choose?

     Function-Calling and Model Context Protocol (MCP) are two mainstream large language model (LLM) integration methods, each with its own unique application scenarios and advantages. They are not substitutes for each other, but complement each other and can play their own value in different business needs and technical environments. Understanding the applicable scenarios of the two will not only help enterprises make wise choices when integrating LLM, but also provide clear guidance for building efficient and reliable intelligent systems.

     So, how to make decisions in actual business scenarios?

     The following are  detailed recommendations on how to choose Function-Calling or MCP, and explore how to combine the two to achieve a better solution.

     1.  Scenarios for using Function-Calling

     Function-Calling has become the preferred method for many specific tasks due to its structured and efficient features. The following are typical scenarios where it is applicable. For details, please refer to:

• Require structured and predictable output: When the task has strict requirements on the format and content of the output, function calls can ensure that the results generated by LLM always meet expectations through predefined function signatures. For example, when it is necessary to return JSON data in a fixed format, function calls can effectively constrain model behavior.

• Tasks with clear boundaries and specific data formats: Function calls work well for tasks with clear goals and fixed data formats. For example, in a data extraction task, the model may need to extract information such as dates and amounts from text and return them in a specific format (such as "YYYY-MM-DD").

• The goal is to seamlessly integrate LLM into existing systems: function calls naturally fit into traditional software architectures, and LLM can be embedded into enterprise systems through clear interfaces (such as APIs). For example, in a scenario where an external service needs to be called, function calls can be directly mapped to API requests.

     Typical cases:

• Data extraction: Extract key information from the text submitted by the user, such as order number or user information.

• Ticket Classification: As mentioned above, classify customer support tickets as "Billing Issue" or "Technical Support".

• API Integration: Get real-time weather data by calling the weather API and return it in a structured format.

    In the above scenarios, Function-Calling can quickly meet business needs with lower development costs and higher controllability.

     2. Scenarios for using MCP

     In contrast, MCP is more suitable for complex, multi-step interaction scenarios due to its flexibility and context management capabilities. The following are typical scenarios where it is applicable:

• Involving complex, multi-step interactions: When a task needs to span multiple steps and each step may depend on the result of the previous step, MCP's hierarchical context management can ensure the coherence and logic of the conversation. For example, an intelligent assistant may need to first confirm the user's needs, then call related services, and finally generate summary suggestions.

• Need to maintain context for a long time: In long conversations or multi-turn interactions, MCP ensures that the model can remember historical information and generate contextually relevant responses through hierarchical context management. For example, in customer support scenarios, MCP can help the model keep track of previous questions asked by users and avoid repeated or contradictory answers.

• Tasks require a balance between creativity and control: MCP allows the model to be guided by contextual constraints while maintaining a certain degree of creativity, which is suitable for scenarios that need to find a balance between openness and norms. For example, in branded conversations, the model needs to show the fluency of natural language while adhering to brand specifications.

     Typical cases:

• Intelligent assistants in specific fields: For example, compliance assistants in the financial field need to provide recommendations that meet regulatory requirements in multiple rounds of conversations.

• Regulatory compliance tools: Ensure that model outputs comply with industry regulations, such as privacy protection requirements in the healthcare field.

• Branded chatbots: Engage in natural, open conversations while maintaining consistency with your brand voice.

     In similar scenarios mentioned above, the flexibility and context-awareness of MCP can significantly improve the quality of interaction and meet complex business needs.

     However, in actual business scenarios, you may face some complex applications, and using  Function-Calling or MCP alone may not fully meet the needs. In this case, combining the two methods can give full play to their advantages while making up for their respective limitations to form a more powerful hybrid solution.

     For example, in a customer support system, the two can be combined in the following way:

     Function-Calling for ticket classification: Using  the structured characteristics of Function-Calling, quickly classify the tickets submitted by users into "billing issues" or "technical support" to ensure the accuracy and consistency of the classification results.

     MCP is used for follow-up questions and context management: After the ticket is classified, the user may ask further questions (such as "How do I solve the billing problem?"). At this time, MCP can track the previous conversation content through hierarchical context management, generate coherent and personalized answers, and ensure that the response complies with brand specifications.

    This hybrid approach can bring out the advantages of each at different stages: Function-Calling ensures the efficiency and controllability of key tasks, while MCP enhances the flexibility and contextual coherence of conversations. Through proper design, developers can seamlessly integrate these two approaches in the system architecture, such as passing the results to the context layer of MCP after the function call is completed for subsequent processing.

     In summary, Function-Calling and MCP each have their own areas of expertise, and the method you choose depends on specific business needs and technical goals.

     If the task requires highly structured output and fast integration, Function-Calling is a better choice; if the task involves complex interactions, long-term context management, or requires a balance between creativity and control, MCP is more advantageous. In some comprehensive scenarios, combining the two can achieve higher efficiency and flexibility, providing a more comprehensive solution for the practical application of LLM. When making a choice, enterprises should fully evaluate the complexity of the task, the compatibility of the system architecture, and the need for controllability and creativity to ensure that the final solution can maximize the satisfaction of business goals.