MCP Server And Tools

MCP Server and Tools is a project for building agents and complex workflows based on large language models (LLMs). It integrates with real - time data and tools through the Educhain library and supports the generation of educational content such as multiple - choice questions, lesson plans, and flashcards.

Education and learning tools Artificial intelligence chatbots #Educational Tools #LLM Integration #Real - time Data #Workflows .Python

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update time : 2025-04-23

What is MCP Server?

MCP (Model Context Protocol) is a bridge service that enables large language models to integrate with external data and tools. It allows AI assistants to access the latest data, educational tools, and multiple LLM vendors through a standardized protocol.

How to use MCP Server?

Interact with the MCP Server through a simple STDIO standard input - output interface. The client sends standardized requests, and the server returns structured responses. Built - in tools can be directly called via the API.

Use cases

It is particularly suitable for the educational technology field, such as automatically generating teaching materials, creating personalized learning content, and AI application scenarios that require real - time data access.

Main Features

Real - time Data IntegrationEnables LLMs to access and utilize the latest information outside of the training data

Pre - built Educational ToolsOut - of - the - box MCQ generators, lesson plan makers, and flashcard tools

Vendor - agnosticSupports flexible switching between different LLM providers

Advantages and Limitations

Advantages

Simplify the integration of LLMs with real - world data

Provide specially optimized tools for the education field

Avoid vendor lock - in and flexibly select AI models

Limitations

The current toolset is mainly oriented towards educational scenarios

Basic programming knowledge is required for integration

Some advanced features rely on third - party APIs

How to Use

Install Dependencies

Use the uv tool to install the Educhain library and MCP Server

Configure API Keys

Set the API keys of Groq or other LLM providers in the configuration file

Start the Server

Run the main program to start the MCP service

Usage Examples

Automatic Quiz GenerationTeachers can quickly create classroom quiz questions

Course PreparationAutomatically generate a complete teaching syllabus

Frequently Asked Questions

Is programming experience required to use it?

Which language models are supported?

How to ensure the quality of the generated content?

Related Resources

Educhain GitHub Repository

Core educational content generation library

MCP Official Documentation

Protocol specifications and API references

Python SDK

Python implementation of MCP

🚀 MCP Server and Tools

MCP (Model Context Protocol) helps you build agents and complex LLM-based workflows. LLMs often need to integrate with data and tools, and MCP offers:

The ability to update the LLM with current or relevant yet unknown data.
A growing list of pre-built integrations that your LLM can plug into directly.
The flexibility to switch between different LLM providers and vendors.
Best practices for protecting data in your infrastructure.

📂 File Structure

main.py - The main file to run the MCP server.
Task1 setup - A folder containing a single educhain function and generating responses in the required format for the MCP server.
Response - The response folder, containing all output images and required PDFs.

📋 Overview

MCP runs on STDIO, where standardized input is passed to the server, which returns standardized output managed by the client. The MCP server and tools are designed to enable seamless interaction between LLMs or AI assistants and real-world data, databases, and news. Through the Educhain library, this project provides a powerful framework for building AI-based educational tools and services.

✨ Features

MCP Server

Serves as a bridge between LLMs and external data sources.
Enables support for real-time access to databases, news, and other dynamic information.

MCP Tools (in the project)

The current tools rely on the Groq API and open-source models, but we can use any number of LLMs if needed:

MCQ Generator: Automatically generates multiple-choice questions based on the input topic and the required number of MCQs. We can also provide additional information or guidance to steer the MCQ generation process.
Lesson Plan Generator: Creates structured lesson plans customized for specific topics or requirements.
Flashcard Generator: Generates flashcards for effective learning and review.

🚧 Project Workflow

After installing the Educhain library and setting up the API, I created an educhain client using the LLama model. Then, I initialized the MCP tools through an asynchronous function and attached appropriate docstrings so that the LLM can select the right tools and generate the required content based on the output.

💼 Use Cases

This project allows AI assistants (e.g., Claude Desktop as the MCP host here) to utilize MCP tools for educational purposes. For example:

Generate multiple-choice questions for quizzes.
Prepare lesson plans for teachers or students.
Create flashcards for learning and review.

🛠️ Tech Stack

uv: Used for basic installation and running the application.
Educhain Library: The core library for building the MCP server and tools.
MCP Server: A lightweight program that exposes specific functions through the standardized Model Context Protocol.

📚 References

Educhain Library: The core library for generating educational content.
MCP Server: Refer to the quick-start guide to start using the MCP server and tools.
MCP Website: Refer to how to set up tools in MCP.