Add Api Key To .env File
This project implements a minimized MCP project to understand how the MCP system works. The project calls the LLM service through Deepseek's API_KEY, supports multi - task processing, and includes example services such as weather and stock queries.
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What is MCP?
The Model Context Protocol (MCP) is a system that allows different AI services to work together through a standardized protocol. This demo shows the simplest implementation to help understand how the MCP system works.How to use this MCP demo?
This demo connects to the Deepseek artificial intelligence service (or other compatible large language model services) to demonstrate its functions. By configuring different commands, various task processing can be achieved.Use Cases
This demo is suitable for the following scenarios: 1) Demonstrate the basic functions of the MCP protocol; 2) Test the compatibility of different LLM services; 3) Verify the scalability and flexibility of the system. By running different commands, multiple tasks can be achieved, including querying the weather and getting stock information.Features
Multi - service SupportIt can connect to and use multiple large language model (LLM) services, including but not limited to Deepseek.
Modular DesignDefine different task - processing commands through the configuration file (server.json) to achieve flexible expansion of the system. Each task can run independently or be used in combination.
Advantages and Disadvantages
How to Use
Usage Examples
Frequently Asked Questions
Which LLM services are currently supported?
How to add new task - processing functions?
Where can I get the API key?
More Resources
MCP Official Documentation
View the complete MCP protocol specification and usage guide.
Deepseek Developer Center
Get the latest API documentation, toolkits, and support resources.
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