Student Assistant MCP Server

A local Model Context Protocol (MCP) server built with Python that allows AI assistants to calculate CGPA, view assignment deadlines, and search subject notes.

Features

• Calculate CGPA from semester SGPA values

• Read assignment deadlines from a JSON file

• Filter deadlines by subject

• Search local subject notes

• Connect with MCP-compatible clients such as VS Code

• Test tools using MCP Inspector

Related MCP server: Student MCP Server

Tech Stack

• Python 3.10+

• MCP Python SDK

• uv package manager

• JSON and text files

• MCP Inspector

• Visual Studio Code

Project Structure

student-assistant-mcp/
├── .vscode/
│   └── mcp.json
├── notes/
│   ├── daa.txt
│   └── dbms.txt
├── src/
│   └── student_assistant_mcp/
│       ├── __init__.py
│       └── server.py
├── .gitignore
├── .python-version
├── deadlines.json
├── pyproject.toml
├── README.md
└── uv.lock

Available MCP Tools

calculate_cgpa

Calculates an estimated CGPA from a list of semester SGPA values.

Example input:

{
  "semester_sgpas": [8.5, 8.9, 9.1]
}

Example output:

Your estimated CGPA is 8.83.

The current calculation treats every semester as having equal weight.

get_upcoming_deadlines

Reads deadlines from deadlines.json and optionally filters them by subject.

Example input:

{
  "subject": "DAA"
}

Example output:

DAA: Complete Assignment 4 — 2026-09-15

search_notes

Searches for a word or phrase inside a subject's text file.

Example input:

{
  "subject": "DAA",
  "query": "negative"
}

Example output:

It does not work correctly with negative edge weights.

Installation

1. Clone the repository

git clone https://github.com/apurva8255/student-assistant-mcp.git
cd student-assistant-mcp

2. Install uv

Follow the installation guide at:

https://docs.astral.sh/uv/getting-started/installation/

On Windows PowerShell:

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Restart the terminal after installation.

3. Install dependencies

uv sync

Running the MCP Server

uv run python -m student_assistant_mcp.server

The terminal may remain blank because the server communicates through standard input and output and waits for an MCP client.

Press Ctrl + C to stop the server.

Testing with MCP Inspector

Node.js 22.19 or newer is required for the current MCP Inspector.

Run:

npx @modelcontextprotocol/inspector uv run python -m student_assistant_mcp.server

Open the local URL shown in the terminal, connect to the server, and select Tools → List Tools.

Connecting to VS Code

Create .vscode/mcp.json with the following configuration:

{
  "servers": {
    "studentAssistant": {
      "type": "stdio",
      "command": "uv",
      "args": [
        "run",
        "python",
        "-m",
        "student_assistant_mcp.server"
      ],
      "cwd": "${workspaceFolder}"
    }
  }
}

In VS Code:

1. Open the Command Palette using Ctrl + Shift + P.

2. Run MCP: List Servers.

3. Select studentAssistant.

4. Start the server.

5. Open VS Code Chat in Agent mode.

Example prompts:

Calculate my CGPA for SGPAs 8.5, 8.9 and 9.1.

Show my DAA deadlines.

Search my DAA notes for negative edge weights.

How It Works

The AI assistant discovers the tools exposed by the MCP server. When a user asks a relevant question, the AI selects a tool and supplies the required arguments.

The Python server executes the function, reads any required local data, and returns the result to the AI assistant.

User → AI assistant → MCP server → Python tool
User ← AI response  ← Tool result ←─────────

Future Improvements

• Credit-weighted CGPA calculation

• Automatic deadline sorting

• Completed-task tracking

• PDF and DOCX note search

• Semantic search using embeddings

• SQLite database integration

• Google Calendar integration

• Deadline reminders

• Remote deployment with authentication

Author

Apurva Sidhawar
B.Tech Computer Science and Engineering — Artificial Intelligence and Machine Learning
Vishwakarma Institute of Technology, Pune