A

AI Tutor

AI Tutor is an AI tutoring system for higher education based on MCP client/server and multi-agent collaboration. It supports Claude and OpenAI models and provides a flexible server configuration method.
2 points
9

What is AI Tutor MCP Server?

AI Tutor is an intelligent tutoring system for higher education based on the Model Context Protocol (MCP). Through a client/server architecture and multi-agent collaboration, it provides AI-driven personalized learning support for teachers and students. The system supports multiple large language models such as Claude and OpenAI.

How to use AI Tutor?

You can add various MCP services through a simple JSON configuration file. It supports two connection methods: standard input/output (STDIO) and server-sent events (SSE). The front-end can interact with these services through the REST API.

Use cases

It is suitable for scenarios that require AI-assisted teaching, such as online learning platforms, intelligent tutoring systems, academic writing assistants, and course Q&A systems in higher education institutions.

Main features

Multi-model supportIt supports both Claude series and OpenAI series of large language models, which can be flexibly selected according to requirements.
Multi-protocol connectionIt supports two communication protocols, STDIO and SSE, to adapt to different deployment environments.
Plug and playYou can add new services through simple JSON configuration without modifying the core code.
Agent collaborationMultiple AI agents can work together to provide composite intelligent services.

Advantages and limitations

Advantages
Flexible architecture design, easy to expand new functions
Supports mainstream AI models, avoiding vendor lock-in
Simple configuration method reduces the technical threshold
Multi-agent collaboration provides more comprehensive services
Limitations
Basic programming knowledge is required for configuration
Performance depends on the capabilities of the selected AI model
Local deployment requires certain computing resources

How to use

Prepare the configuration file
Create or modify the config.json file and add the required MCP services.
Configure the STDIO service
For local Python script services, specify the interpreter and script path.
Configure the SSE service
For remote SSE services, just provide the endpoint URL.
Start the service
Run the main program to load the configuration file and start all services.

Usage examples

Time and date serviceA simple service example to get the current time and date
GPA calculation serviceAn academic service for calculating students' GPA
Thesis writing assistanceMulti-agent collaboration to assist academic writing

Frequently Asked Questions

How to add a custom AI model service?
Which programming languages are supported for service development?
How to monitor the running status of the service?
Does it support dynamic loading and unloading of services?

Related resources

MCP protocol specification
The complete technical specification document of the Model Context Protocol
GitHub repository
Project source code and example implementations
Configuration guide video
Step-by-step demonstration of how to configure various MCP services
Installation
Copy the following command to your Client for configuration
Note: Your key is sensitive information, do not share it with anyone.
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