home / mcp / educhain mcp server
Provides an EduChain powered MCP server to generate MCQs, lesson plans, and flashcards via standardized MCP tools.
Configuration
View docs{
"mcpServers": {
"agusain2001-educhain-mcp-assignment": {
"command": "python3.11",
"args": [
"/home/ubuntu/mcp_educhain_server.py"
],
"env": {
"OPENAI_API_KEY": "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"
}
}
}
}You have an MCP server that exposes EduChainβs educational content generation capabilities through standardized tools. It lets clients generate MCQs, lesson plans, and flashcards, enabling seamless integration with MCP-compatible clients and workflows.
You run the server locally and connect an MCP client to access EduChain capabilities. Start by launching the server so clients can request content generation. You can then issue requests to generate MCQs, lesson plans, or flashcards. Typical usage patterns include asking for a specific topic and desired amount, then handling the structured JSON responses that the tools return. For testing and demonstration, you can invoke commands like generating a batch of MCQs on a topic, or requesting a complete lesson plan for a given topic and duration. Claude Desktop users can configure their MCP client to point at this server and issue natural-language prompts that map to the available tools.
When you want to start a session, ensure the server is running in the foreground so your MCP client can connect. The server exposes three core tools you can call through the MCP interface: generate_mcqs, generate_lesson_plan, and generate_flashcards. Each tool returns data in JSON format with questions, answers, and explanations, or structured lesson plans and flashcards suitable for study workflows.
pip install -r requirements.txtVerify Python 3.11 is installed on your system. Then start the MCP server using the provided script.
python3.11 mcp_educhain_server.pyOptionally run tests to confirm functionality before production usage.
python3.11 test_mcp_server.pyConfigure Claude Desktop (or your MCP client) by placing the configuration file in the appropriate directory so the client can connect to your local MCP server.
The server is designed to run locally and communicate with MCP clients through standard tooling. It requires an API key to access the AI model backend, which is supplied via environment variables in the runtime configuration. You can adjust prompts or topic inputs to tailor the generated educational content to your needs.
Key tools exposed through the MCP interface include: generate_mcqs for multiple-choice questions, generate_lesson_plan for structured lesson content, and generate_flashcards for quick study aids. Each tool returns structured JSON that your client can render in your application or educational workflow.
Generate 5 multiple-choice questions on Python loops.
Provide a lesson plan for teaching algebra that fits a 60-minute class for intermediate students.
Create flashcards for Python data types.
Generate multiple-choice questions on a specified topic. Returns JSON with questions, options, answers, and explanations.
Create a comprehensive lesson plan for a topic, including objectives, structure, and materials.
Generate a set of flashcards with questions and answers for study.