Anudeep-CodeSpace/educhain_mcp_server

Educhain based MCP Server (via Google Gemini)

This project devises an MCP server that handles various functions like: generating MCQs, flashcards, lesson-plans etc.,

Structure

Claude Desktop(Front end) <-> MCP Server <-> LLM (google Gemini)

Installation and Initialization

Recommended Python version: 3.10

Packages Manager: uv (Recommended) or pip

Step 0: Clone this repository

git clone Anudeep-CodeSpace/educhain_mcp_server.git
cd educhain_mcp_server

Step 1: Install uv

pip install uv # universal
brew install uv # mac os only

Step 2: Initialize project

uv init # initialize an already existing project

Step 3: Add required packages

# They contain all the required sub -packages in them
uv add "educhain" "mcp[cli]"

Step 4: Add your Google gemini api key

# inside .env file
GOOGLE_API_KEY=<Your Google api key without quotes>

Step 5: Debug your MCP server

uv run mcp dev main.py

It produces a tokenised proxy server at

http://localhost:6274/?PROXY_API_TOKEN=<proxy token>

Paste it and navigate to the link in a browser. Click "Connect" and you can debug your tools, resources and prompts in that site.

Step 6: Install Claude Desktop app

Install Claude Desktop app and login with your account(can be new).

Step 7: Add MCP server to Claude Desktop app

In the git repo folder run

# Adds the MCP Server to Claude Desktop client
uv run mcp install main.py

After that your claude_desktop_config.json should look like this:

{
  "mcpServers": {
    "Educhain - MCP server": {
      "command": "absolute/path/to/uv",
      "args": [
        "run",
        "--with",
        "mcp[cli]",
        "mcp",
        "run",
        "absolute/path/to/main/main.py"
      ]
    }
  }
}

Final step: Check for any discrepancies in the logs

All the logs are located at:

%APPDATA%\\Claude\\logs\\mcp.log # in windows
~/Library/Logs/Claude/mcp.log # in macOS

Metadata

get_info(about://info) resource lists out all the tools and resources provided by this server

Key Characteristics of this project

Modularity: Separating the server initialization (server.py), route handling (handlers.py), and the main entry point (main.py) makes the codebase clean, scalable, and easy to maintain.

Clear Schema Definitions: Use of Pydantic models in the schema directory. It ensures strong data validation, clear API contracts, and self-documenting code for requests and responses.

Dependency Injection: Passing the mcp server instance to handler functions (handle_resources(mcp)) is a good practice. It avoids circular dependencies and global state issues.

Use of Decorators: The @mcp.resource and @mcp.tool decorators provide a clean and declarative way to define the server's capabilities.

Known Issues

Claude Desktop client cannot direclty access Resource Templates (Beta stage)

For Example Claude Desktop client cannot access the generate_lessonplan resource(uri = lessonplan://{topic}) cannot be used directly as it is in beta stage and doesn't support dynamic resource uri's!!!

So Generate a lesson plan to teach algebra cannot invoke the generate_lessonplan tools!

Needs external LLM to generate content

Claude being a powerful llm cannot direclty generate content according to our tools and resources! (Hence I am using Gemini)

Key Contributors

Myself(Anudeep-CodeSpace), Chatgpt, Perplexity AI, Gemini(Free LLM)

Note

Node js(LTS) version is required for debugging pyenv is not recommended(That wasted a lot of time for me 😭)