Student Progress Tracker MCP Server

MCP server for AI-powered student progress tracking and learning analytics. Connects to Claude Desktop or Claude Code to give AI assistants the ability to manage students, track assessments, calculate mastery levels, identify learning gaps, and recommend focus areas.

What is MCP?

Model Context Protocol (MCP) is an open standard that lets AI assistants like Claude connect to external tools and data sources. This server exposes student progress tracking as MCP tools that Claude can call directly during conversations.

Related MCP server: EduChain MCP Server

Features

• Student profiles - Create and manage student records with grade levels

• Course enrollment - Organize students into courses with topics

• Assessment tracking - Record tests, quizzes, homework, and practice results

• Mastery levels - Automatic topic mastery calculation from question-level data

• Learning gap analysis - Identify topics below threshold with severity ratings

• Focus recommendations - Prioritized study suggestions based on trends

• Class analytics - Grade distribution and topic performance across a class

• Question-level telemetry - Track correctness, time, difficulty, and Bloom's level per question

Quick Start

Install

git clone https://github.com/woodstocksoftware/student-progress-tracker.git
cd student-progress-tracker
python3.12 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Configure Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "student-progress": {
      "command": "/absolute/path/to/student-progress-tracker/venv/bin/python",
      "args": ["/absolute/path/to/student-progress-tracker/run_server.py"]
    }
  }
}

Restart Claude Desktop. The tools will appear automatically.

Configure Claude Code

Add to your project's .mcp.json or global settings:

{
  "mcpServers": {
    "student-progress": {
      "command": "/absolute/path/to/student-progress-tracker/venv/bin/python",
      "args": ["/absolute/path/to/student-progress-tracker/run_server.py"]
    }
  }
}

Try It

You: Create a student named Alice in 9th grade
You: Create a course called Algebra I in Mathematics
You: Enroll Alice in Algebra I
You: Create a topic "Linear Equations" in the Algebra course
You: Record Alice's quiz: 85 out of 100
You: What are Alice's learning gaps?
You: What should Alice focus on next?

Tool Reference

Student Management

create_student

Create a new student record.

Create a student named Alice Johnson, email alice@school.edu, in 9th grade

list_students

List all students, optionally filtered by course.

get_student_profile

Comprehensive profile with courses, statistics, and recent assessments.

Course Management

create_course

Create a new course.

list_courses

List all courses with enrolled student counts.

enroll_student

Enroll a student in a course.

create_topic

Add a topic within a course for mastery tracking.

list_topics

List all topics in a course.

Assessment & Results

create_assessment

Create a test, quiz, homework, or practice assessment.

record_assessment_result

Record a student's assessment result with optional question-level data.

Each item in question_results:

{
  "question_id": "q1",
  "topic_id": "topic-abc123",
  "is_correct": true,
  "time_spent_seconds": 45,
  "difficulty": 0.5,
  "bloom_level": "application"
}

Analytics & Recommendations

get_topic_mastery

View mastery levels by topic with trend indicators.

get_learning_gaps

Identify topics below a mastery threshold.

get_student_history

Question-level performance history.

get_class_analytics

Class-wide performance overview with grade distribution and topic analysis.

recommend_focus_areas

Personalized study recommendations prioritized by trend and mastery level.

Data Model

Students ──── Enrollments ──── Courses ──── Topics
   │                              │           │
   │                              │           │
   └──── Assessment Results ──── Assessments  │
              │                               │
              │                               │
         Question History ──── Topic Mastery ─┘

Tables

Mastery Calculation

Mastery is calculated as questions_correct / questions_attempted per topic. Trends are detected by comparing recent accuracy to overall mastery:

• Improving - Recent accuracy > mastery + 10%

• Stable - Within 10% of mastery

• Declining - Recent accuracy < mastery - 10%

Usage Examples

Track a Student's Math Mastery Over Time

1. Create a student and course
2. Add topics: Linear Equations, Quadratic Equations, Polynomials
3. Record quiz results with question_results linking to topics
4. View mastery: "Show me Alice's topic mastery for Algebra I"
5. Check gaps: "What are Alice's learning gaps?"
6. Get recommendations: "What should Alice focus on next?"

Monitor a Class

1. Create a course with multiple enrolled students
2. Record assessment results for each student
3. View analytics: "Show class analytics for Algebra I"
4. See grade distribution and struggling topic areas

Architecture

Claude (Desktop/Code)
    │
    │ MCP Protocol (stdio)
    │
    ▼
┌─────────────────────────────┐
│  server.py (FastMCP)        │
│  15 tools with @mcp.tool()  │
│  Markdown response format   │
└─────────────┬───────────────┘
              │
              ▼
┌─────────────────────────────┐
│  database.py (SQLite)       │
│  10 tables, auto-init       │
│  Foreign keys + indexes     │
└─────────────┬───────────────┘
              │
              ▼
┌─────────────────────────────┐
│  data/student_progress.db   │
│  Local file (gitignored)    │
└─────────────────────────────┘

Key design decisions:

• FastMCP decorators for tool registration with auto-generated JSON schemas

• stdio transport for communication with Claude

• SQLite for zero-config local persistence

• Markdown responses with emoji indicators for readability in chat

• UUID-prefixed IDs (stu-, course-, topic-, assess-, result-) for clarity

• JSON question_results for flexible per-question data storage

Integration with Question Bank MCP

Designed to compose with Question Bank MCP:

Question Bank MCP              Student Progress Tracker
┌───────────────────┐          ┌───────────────────────┐
│ Questions         │          │ Students              │
│ Topics            │◄────────►│ Enrollments           │
│ Difficulty levels │          │ Assessment Results    │
│ Bloom's taxonomy  │          │ Topic Mastery         │
└───────────────────┘          └───────────────────────┘

Link them via question_bank_id when creating a course.

Testing

source venv/bin/activate
pip install pytest
pytest tests/ -v

Contributing

1. Fork the repository

2. Create a feature branch (git checkout -b feature/your-feature)

3. Make your changes with type hints and docstrings

4. Run linting: ruff check src/

5. Run tests: pytest tests/ -v

6. Commit and open a pull request

License

MIT

Part of the Ed-Tech MCP Suite by Jim Williams | GitHub