The difference between Function Call, Tools and MCP in large models

The differences and usage scenarios of Function Call, Tools and MCP in large models are as follows:

Differences

- Function Call: It is a unique interface feature provided by some large models (such as OpenAI's GPT-4). It allows LLM to generate function call requests in a specific format, and the host executes the operation and returns the result. The message format is usually fixed JSON, containing function_name and parameters fields.
- Tools: It is an external resource that can be called by large models, such as database query tools, file reading tools, etc. In different frameworks, the definition and use of tools are different. For example, LangChain is based on the Tool class abstraction, supports arbitrary function signatures, and dynamically generates parameters through prompt.
- MCP: The Model Context Protocol is a standardized protocol proposed by Anthropic. It defines the standard message format and interaction process for large models to interact with external tools. It uses a format similar to JSON-RPC 2.0, supports custom binary/text formats, and realizes inter-process communication through standard input and output. It is suitable for the decoupled integration of models, tools, and services.

Usage scenarios

- Function Call: It is suitable for integration with Web services and quick implementation of some simple functional extensions, such as calling existing APIs to obtain data. For example, in a chatbot based on GPT-4, when a user asks for specific information, the corresponding API can be called through Function Call to obtain accurate data and answer the user.
- Tools: used in various scenarios that require large models to interact with external resources, such as data processing, file operations, etc. Different tools are suitable for different specific tasks, such as text processing tools can be used to analyze and edit documents, and image recognition tools can be used to process image-related tasks.
- MCP: suitable for scenarios with high requirements for performance and cross-language compatibility, and where models and tools need to be deployed independently. For example, in enterprise-level applications, AI models need to be integrated with backend services written in multiple different languages ​​to automate complex business processes. If

the user's call selection for asking about the weather

is based on a large model that supports Function Call, such as GPT-4, Function Call can be used to call the weather query API to obtain weather information, because Function Call is more convenient in calling Web services to obtain simple data. If it is in a system using MCP, the weather query service provided by MCP Server can also be used to obtain weather. MCP can well achieve the decoupling and standardized interaction between models and external tools. If it is based on a framework such as LangChain, you can use its tool integration function to integrate the weather query tool and call it through the framework's interface to obtain weather information.