Step 1: Initial TutorX MCP server setup with basic core features

.gitignore

@@ -0,0 +1,10 @@
+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Virtual environments
+.venv

.python-version

@@ -0,0 +1 @@
+3.12

README.md

@@ -0,0 +1,55 @@
+# TutorX-MCP Server
+
+A Model Context Protocol (MCP) server for educational AI tutoring as specified in the PRD.
+
+## Overview
+
+TutorX-MCP is an adaptive, multi-modal, and collaborative AI tutoring platform that leverages the Model Context Protocol (MCP) for tool integration and provides APIs for educational features.
+
+## Features
+
+- **Adaptive Learning Engine**: Concept graph, skill assessment, and personalized learning paths
+- **Assessment Suite**: Quiz generation, solution analysis
+- **Feedback System**: Error pattern analysis and contextual suggestions
+- **Multi-Modal Interaction**: Text-based Q&A (with planned voice and handwriting recognition)
+
+## Getting Started
+
+### Prerequisites
+
+- Python 3.12 or higher
+- Dependencies as listed in pyproject.toml
+
+### Installation
+
+```bash
+# Clone the repository
+git clone https://github.com/yourusername/tutorx-mcp.git
+cd tutorx-mcp
+
+# Install dependencies
+uv install
+```
+
+### Running the Server
+
+```bash
+python main.py
+```
+
+By default, the server will run in development mode and you can access it at http://localhost:8000.
+
+## MCP Tool Integration
+
+The server exposes MCP tools for:
+- Skill assessment
+- Quiz generation
+- Error pattern analysis
+
+And MCP resources for:
+- Concept graph
+- Learning paths
+
+## License
+
+This project is licensed under the MIT License - see the LICENSE file for details.

docs/prd.md

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+# TutorX-MCP
+# Product Requirements Document (PRD)
+
+**Educational AI Tutor MCP Server**
+
+## 1. Overview
+
+**Product Name:**
+
+Educational AI Tutor MCP Server
+
+**Purpose:**
+
+To provide an adaptive, multi-modal, and collaborative AI tutoring platform accessible via browser, leveraging Model Context Protocol (MCP) for tool integration and Gradio for user-friendly interfaces.
+
+**Target Users:**
+
+- **Students:** K-12, higher education, lifelong learners
+- **Teachers:** For classroom integration and progress monitoring
+- **Administrators:** For curriculum management and analytics
+
+---
+
+## 2. Objectives
+
+- **Deliver personalized, adaptive learning experiences**
+- **Enable collaborative and interactive learning**
+- **Support multiple input/output modalities (text, voice, handwriting, AR/VR)**
+- **Ensure privacy and data security**
+- **Integrate with external educational tools and standards**
+- **Provide actionable insights and analytics for students and teachers**
+
+---
+
+## 3. Features
+
+## 3.1 Core Features
+
+- **Adaptive Learning Engine**
+    - Concept graph with 50,000+ nodes (STEM and humanities)
+    - Dynamic skill assessment and competency tracking
+    - Personalized learning paths based on real-time feedback
+- **Multi-Modal Interaction**
+    - Text-based Q&A with error pattern recognition
+    - Voice recognition with ASR and TTS
+    - Handwriting recognition and digital ink processing
+- **Assessment Suite**
+    - Automated quiz and problem generation
+    - Step-by-step solution analysis
+    - Plagiarism and similarity detection
+- **Feedback System**
+    - Contextual error analysis and suggestions
+    - Multimodal feedback (text, audio, visual)
+    - Emotional state recognition (via webcam/EEG)
+
+## 3.2 Advanced Features
+
+- **Neurological Engagement Monitor**
+    - Integration with consumer-grade EEG devices
+    - Attention, cognitive load, and stress detection
+- **Cross-Institutional Knowledge Fusion**
+    - Curriculum alignment with 10+ national standards
+    - Textbook content reconciliation
+    - Cultural adaptation engine
+- **Automated Lesson Authoring**
+    - AI-powered content generation from PDFs, videos, web
+
+## 3.3 User Experience
+
+- **Custom Dashboard**
+    - Knowledge growth map
+    - Temporal performance heatmap
+    - Cognitive profile radar
+- **Accessibility**
+    - Screen reader compatibility
+    - Text-to-speech and adjustable interface
+- **Gamification**
+    - Badges, leaderboards, token-based rewards
+
+---
+
+## 4. Technical Requirements
+
+- **MCP Server:** Exposes all core and advanced features as MCP tools
+- **Gradio Interface:** User-friendly, customizable, and accessible
+- **Microservices Architecture:** Modular design for scalability
+- **Real-Time Data Processing:** Asynchronous task queues and caching
+- **Cloud Deployment:** Browser-based access with no local setup
+- **Integration:** Supports external APIs, LMS, and educational tools
+
+---
+
+## 5. Non-Functional Requirements
+
+- **Performance:** Supports 100+ concurrent users with <1s response time for core features
+- **Security:** End-to-end encryption, role-based access control
+- **Privacy:** Compliant with GDPR, COPPA, and FERPA
+- **Scalability:** Horizontally scalable to support large institutions
+- **Accessibility:** WCAG 2.1 AA compliant
+
+---
+
+## 6. Success Metrics
+
+- **User Engagement:** Average session duration, repeat usage
+- **Learning Outcomes:** Improvement in quiz/test scores
+- **Adoption Rate:** Number of active users and institutions
+- **Teacher Satisfaction:** Feedback on usability and effectiveness
+- **Technical Performance:** Uptime, response time, error rates
+
+---
+
+## 7. Roadmap
+
+1. **Phase 1:** Core adaptive learning engine and MCP integration
+2. **Phase 2:** Multi-modal interaction and collaborative tools
+3. **Phase 3:** Advanced features (engagement monitor, lesson authoring)
+4. **Phase 4:** Deployment, analytics, and gamification
+
+---
+
+## 8. Risks and Mitigation
+
+- **Privacy Concerns:** Implement strict data controls and transparency
+- **Technical Complexity:** Modular design and clear documentation
+- **User Adoption:** Provide training materials and support
+
+---
+
+## 9. Stakeholders
+
+- **Product Manager:** Oversees development and alignment with educational goals
+- **Developers:** Build and maintain the MCP server and Gradio interface
+- **Designers:** Ensure intuitive and accessible user experience
+- **Educators:** Provide feedback and guide curriculum alignment
+- **Students:** End users and primary beneficiaries
+
+---
+
+## 10. Appendix
+
+- **Glossary:** MCP, Gradio, adaptive learning, etc.
+- **References:** Educational standards, privacy regulations, technical documentation
+
+---

docs/sdk.md

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+# MCP Python SDK
+
+<div align="center">
+
+<strong>Python implementation of the Model Context Protocol (MCP)</strong>
+
+[![PyPI][pypi-badge]][pypi-url]
+[![MIT licensed][mit-badge]][mit-url]
+[![Python Version][python-badge]][python-url]
+[![Documentation][docs-badge]][docs-url]
+[![Specification][spec-badge]][spec-url]
+[![GitHub Discussions][discussions-badge]][discussions-url]
+
+</div>
+
+<!-- omit in toc -->
+## Table of Contents
+
+- [Overview](#overview)
+- [Installation](#installation)
+- [Quickstart](#quickstart)
+- [What is MCP?](#what-is-mcp)
+- [Core Concepts](#core-concepts)
+  - [Server](#server)
+  - [Resources](#resources)
+  - [Tools](#tools)
+  - [Prompts](#prompts)
+  - [Images](#images)
+  - [Context](#context)
+- [Running Your Server](#running-your-server)
+  - [Development Mode](#development-mode)
+  - [Claude Desktop Integration](#claude-desktop-integration)
+  - [Direct Execution](#direct-execution)
+  - [Mounting to an Existing ASGI Server](#mounting-to-an-existing-asgi-server)
+- [Examples](#examples)
+  - [Echo Server](#echo-server)
+  - [SQLite Explorer](#sqlite-explorer)
+- [Advanced Usage](#advanced-usage)
+  - [Low-Level Server](#low-level-server)
+  - [Writing MCP Clients](#writing-mcp-clients)
+  - [MCP Primitives](#mcp-primitives)
+  - [Server Capabilities](#server-capabilities)
+- [Documentation](#documentation)
+- [Contributing](#contributing)
+- [License](#license)
+
+[pypi-badge]: https://img.shields.io/pypi/v/mcp.svg
+[pypi-url]: https://pypi.org/project/mcp/
+[mit-badge]: https://img.shields.io/pypi/l/mcp.svg
+[mit-url]: https://github.com/modelcontextprotocol/python-sdk/blob/main/LICENSE
+[python-badge]: https://img.shields.io/pypi/pyversions/mcp.svg
+[python-url]: https://www.python.org/downloads/
+[docs-badge]: https://img.shields.io/badge/docs-modelcontextprotocol.io-blue.svg
+[docs-url]: https://modelcontextprotocol.io
+[spec-badge]: https://img.shields.io/badge/spec-spec.modelcontextprotocol.io-blue.svg
+[spec-url]: https://spec.modelcontextprotocol.io
+[discussions-badge]: https://img.shields.io/github/discussions/modelcontextprotocol/python-sdk
+[discussions-url]: https://github.com/modelcontextprotocol/python-sdk/discussions
+
+## Overview
+
+The Model Context Protocol allows applications to provide context for LLMs in a standardized way, separating the concerns of providing context from the actual LLM interaction. This Python SDK implements the full MCP specification, making it easy to:
+
+- Build MCP clients that can connect to any MCP server
+- Create MCP servers that expose resources, prompts and tools
+- Use standard transports like stdio and SSE
+- Handle all MCP protocol messages and lifecycle events
+
+## Installation
+
+### Adding MCP to your python project
+
+We recommend using [uv](https://docs.astral.sh/uv/) to manage your Python projects. In a uv managed python project, add mcp to dependencies by:
+
+```bash
+uv add "mcp[cli]"
+```
+
+Alternatively, for projects using pip for dependencies:
+```bash
+pip install mcp
+```
+
+### Running the standalone MCP development tools
+
+To run the mcp command with uv:
+
+```bash
+uv run mcp
+```
+
+## Quickstart
+
+Let's create a simple MCP server that exposes a calculator tool and some data:
+
+```python
+# server.py
+from mcp.server.fastmcp import FastMCP
+
+# Create an MCP server
+mcp = FastMCP("Demo")
+
+
+# Add an addition tool
+@mcp.tool()
+def add(a: int, b: int) -> int:
+    """Add two numbers"""
+    return a + b
+
+
+# Add a dynamic greeting resource
+@mcp.resource("greeting://{name}")
+def get_greeting(name: str) -> str:
+    """Get a personalized greeting"""
+    return f"Hello, {name}!"
+```
+
+You can install this server in [Claude Desktop](https://claude.ai/download) and interact with it right away by running:
+```bash
+mcp install server.py
+```
+
+Alternatively, you can test it with the MCP Inspector:
+```bash
+mcp dev server.py
+```
+
+## What is MCP?
+
+The [Model Context Protocol (MCP)](https://modelcontextprotocol.io) lets you build servers that expose data and functionality to LLM applications in a secure, standardized way. Think of it like a web API, but specifically designed for LLM interactions. MCP servers can:
+
+- Expose data through **Resources** (think of these sort of like GET endpoints; they are used to load information into the LLM's context)
+- Provide functionality through **Tools** (sort of like POST endpoints; they are used to execute code or otherwise produce a side effect)
+- Define interaction patterns through **Prompts** (reusable templates for LLM interactions)
+- And more!
+
+## Core Concepts
+
+### Server
+
+The FastMCP server is your core interface to the MCP protocol. It handles connection management, protocol compliance, and message routing:
+
+```python
+# Add lifespan support for startup/shutdown with strong typing
+from contextlib import asynccontextmanager
+from dataclasses import dataclass
+from typing import AsyncIterator
+
+from fake_database import Database  # Replace with your actual DB type
+
+from mcp.server.fastmcp import Context, FastMCP
+
+# Create a named server
+mcp = FastMCP("My App")
+
+# Specify dependencies for deployment and development
+mcp = FastMCP("My App", dependencies=["pandas", "numpy"])
+
+
+@dataclass
+class AppContext:
+    db: Database
+
+
+@asynccontextmanager
+async def app_lifespan(server: FastMCP) -> AsyncIterator[AppContext]:
+    """Manage application lifecycle with type-safe context"""
+    # Initialize on startup
+    db = await Database.connect()
+    try:
+        yield AppContext(db=db)
+    finally:
+        # Cleanup on shutdown
+        await db.disconnect()
+
+
+# Pass lifespan to server
+mcp = FastMCP("My App", lifespan=app_lifespan)
+
+
+# Access type-safe lifespan context in tools
+@mcp.tool()
+def query_db(ctx: Context) -> str:
+    """Tool that uses initialized resources"""
+    db = ctx.request_context.lifespan_context["db"]
+    return db.query()
+```
+
+### Resources
+
+Resources are how you expose data to LLMs. They're similar to GET endpoints in a REST API - they provide data but shouldn't perform significant computation or have side effects:
+
+```python
+from mcp.server.fastmcp import FastMCP
+
+mcp = FastMCP("My App")
+
+
+@mcp.resource("config://app")
+def get_config() -> str:
+    """Static configuration data"""
+    return "App configuration here"
+
+
+@mcp.resource("users://{user_id}/profile")
+def get_user_profile(user_id: str) -> str:
+    """Dynamic user data"""
+    return f"Profile data for user {user_id}"
+```
+
+### Tools
+
+Tools let LLMs take actions through your server. Unlike resources, tools are expected to perform computation and have side effects:
+
+```python
+import httpx
+from mcp.server.fastmcp import FastMCP
+
+mcp = FastMCP("My App")
+
+
+@mcp.tool()
+def calculate_bmi(weight_kg: float, height_m: float) -> float:
+    """Calculate BMI given weight in kg and height in meters"""
+    return weight_kg / (height_m**2)
+
+
+@mcp.tool()
+async def fetch_weather(city: str) -> str:
+    """Fetch current weather for a city"""
+    async with httpx.AsyncClient() as client:
+        response = await client.get(f"https://api.weather.com/{city}")
+        return response.text
+```
+
+### Prompts
+
+Prompts are reusable templates that help LLMs interact with your server effectively:
+
+```python
+from mcp.server.fastmcp import FastMCP
+from mcp.server.fastmcp.prompts import base
+
+mcp = FastMCP("My App")
+
+
+@mcp.prompt()
+def review_code(code: str) -> str:
+    return f"Please review this code:\n\n{code}"
+
+
+@mcp.prompt()
+def debug_error(error: str) -> list[base.Message]:
+    return [
+        base.UserMessage("I'm seeing this error:"),
+        base.UserMessage(error),
+        base.AssistantMessage("I'll help debug that. What have you tried so far?"),
+    ]
+```
+
+### Images
+
+FastMCP provides an `Image` class that automatically handles image data:
+
+```python
+from mcp.server.fastmcp import FastMCP, Image
+from PIL import Image as PILImage
+
+mcp = FastMCP("My App")
+
+
+@mcp.tool()
+def create_thumbnail(image_path: str) -> Image:
+    """Create a thumbnail from an image"""
+    img = PILImage.open(image_path)
+    img.thumbnail((100, 100))
+    return Image(data=img.tobytes(), format="png")
+```
+
+### Context
+
+The Context object gives your tools and resources access to MCP capabilities:
+
+```python
+from mcp.server.fastmcp import FastMCP, Context
+
+mcp = FastMCP("My App")
+
+
+@mcp.tool()
+async def long_task(files: list[str], ctx: Context) -> str:
+    """Process multiple files with progress tracking"""
+    for i, file in enumerate(files):
+        ctx.info(f"Processing {file}")
+        await ctx.report_progress(i, len(files))
+        data, mime_type = await ctx.read_resource(f"file://{file}")
+    return "Processing complete"
+```
+
+## Running Your Server
+
+### Development Mode
+
+The fastest way to test and debug your server is with the MCP Inspector:
+
+```bash
+mcp dev server.py
+
+# Add dependencies
+mcp dev server.py --with pandas --with numpy
+
+# Mount local code
+mcp dev server.py --with-editable .
+```
+
+### Claude Desktop Integration
+
+Once your server is ready, install it in Claude Desktop:
+
+```bash
+mcp install server.py
+
+# Custom name
+mcp install server.py --name "My Analytics Server"
+
+# Environment variables
+mcp install server.py -v API_KEY=abc123 -v DB_URL=postgres://...
+mcp install server.py -f .env
+```
+
+### Direct Execution
+
+For advanced scenarios like custom deployments:
+
+```python
+from mcp.server.fastmcp import FastMCP
+
+mcp = FastMCP("My App")
+
+if __name__ == "__main__":
+    mcp.run()
+```
+
+Run it with:
+```bash
+python server.py
+# or
+mcp run server.py
+```
+
+### Mounting to an Existing ASGI Server
+
+You can mount the SSE server to an existing ASGI server using the `sse_app` method. This allows you to integrate the SSE server with other ASGI applications.
+
+```python
+from starlette.applications import Starlette
+from starlette.routes import Mount, Host
+from mcp.server.fastmcp import FastMCP
+
+
+mcp = FastMCP("My App")
+
+# Mount the SSE server to the existing ASGI server
+app = Starlette(
+    routes=[
+        Mount('/', app=mcp.sse_app()),
+    ]
+)
+
+# or dynamically mount as host
+app.router.routes.append(Host('mcp.acme.corp', app=mcp.sse_app()))
+```
+
+For more information on mounting applications in Starlette, see the [Starlette documentation](https://www.starlette.io/routing/#submounting-routes).
+
+## Examples
+
+### Echo Server
+
+A simple server demonstrating resources, tools, and prompts:
+
+```python
+from mcp.server.fastmcp import FastMCP
+
+mcp = FastMCP("Echo")
+
+
+@mcp.resource("echo://{message}")
+def echo_resource(message: str) -> str:
+    """Echo a message as a resource"""
+    return f"Resource echo: {message}"
+
+
+@mcp.tool()
+def echo_tool(message: str) -> str:
+    """Echo a message as a tool"""
+    return f"Tool echo: {message}"
+
+
+@mcp.prompt()
+def echo_prompt(message: str) -> str:
+    """Create an echo prompt"""
+    return f"Please process this message: {message}"
+```
+
+### SQLite Explorer
+
+A more complex example showing database integration:
+
+```python
+import sqlite3
+
+from mcp.server.fastmcp import FastMCP
+
+mcp = FastMCP("SQLite Explorer")
+
+
+@mcp.resource("schema://main")
+def get_schema() -> str:
+    """Provide the database schema as a resource"""
+    conn = sqlite3.connect("database.db")
+    schema = conn.execute("SELECT sql FROM sqlite_master WHERE type='table'").fetchall()
+    return "\n".join(sql[0] for sql in schema if sql[0])
+
+
+@mcp.tool()
+def query_data(sql: str) -> str:
+    """Execute SQL queries safely"""
+    conn = sqlite3.connect("database.db")
+    try:
+        result = conn.execute(sql).fetchall()
+        return "\n".join(str(row) for row in result)
+    except Exception as e:
+        return f"Error: {str(e)}"
+```
+
+## Advanced Usage
+
+### Low-Level Server
+
+For more control, you can use the low-level server implementation directly. This gives you full access to the protocol and allows you to customize every aspect of your server, including lifecycle management through the lifespan API:
+
+```python
+from contextlib import asynccontextmanager
+from typing import AsyncIterator
+
+from fake_database import Database  # Replace with your actual DB type
+
+from mcp.server import Server
+
+
+@asynccontextmanager
+async def server_lifespan(server: Server) -> AsyncIterator[dict]:
+    """Manage server startup and shutdown lifecycle."""
+    # Initialize resources on startup
+    db = await Database.connect()
+    try:
+        yield {"db": db}
+    finally:
+        # Clean up on shutdown
+        await db.disconnect()
+
+
+# Pass lifespan to server
+server = Server("example-server", lifespan=server_lifespan)
+
+
+# Access lifespan context in handlers
+@server.call_tool()
+async def query_db(name: str, arguments: dict) -> list:
+    ctx = server.request_context
+    db = ctx.lifespan_context["db"]
+    return await db.query(arguments["query"])
+```
+
+The lifespan API provides:
+- A way to initialize resources when the server starts and clean them up when it stops
+- Access to initialized resources through the request context in handlers
+- Type-safe context passing between lifespan and request handlers
+
+```python
+import mcp.server.stdio
+import mcp.types as types
+from mcp.server.lowlevel import NotificationOptions, Server
+from mcp.server.models import InitializationOptions
+
+# Create a server instance
+server = Server("example-server")
+
+
+@server.list_prompts()
+async def handle_list_prompts() -> list[types.Prompt]:
+    return [
+        types.Prompt(
+            name="example-prompt",
+            description="An example prompt template",
+            arguments=[
+                types.PromptArgument(
+                    name="arg1", description="Example argument", required=True
+                )
+            ],
+        )
+    ]
+
+
+@server.get_prompt()
+async def handle_get_prompt(
+    name: str, arguments: dict[str, str] | None
+) -> types.GetPromptResult:
+    if name != "example-prompt":
+        raise ValueError(f"Unknown prompt: {name}")
+
+    return types.GetPromptResult(
+        description="Example prompt",
+        messages=[
+            types.PromptMessage(
+                role="user",
+                content=types.TextContent(type="text", text="Example prompt text"),
+            )
+        ],
+    )
+
+
+async def run():
+    async with mcp.server.stdio.stdio_server() as (read_stream, write_stream):
+        await server.run(
+            read_stream,
+            write_stream,
+            InitializationOptions(
+                server_name="example",
+                server_version="0.1.0",
+                capabilities=server.get_capabilities(
+                    notification_options=NotificationOptions(),
+                    experimental_capabilities={},
+                ),
+            ),
+        )
+
+
+if __name__ == "__main__":
+    import asyncio
+
+    asyncio.run(run())
+```
+
+### Writing MCP Clients
+
+The SDK provides a high-level client interface for connecting to MCP servers:
+
+```python
+from mcp import ClientSession, StdioServerParameters, types
+from mcp.client.stdio import stdio_client
+
+# Create server parameters for stdio connection
+server_params = StdioServerParameters(
+    command="python",  # Executable
+    args=["example_server.py"],  # Optional command line arguments
+    env=None,  # Optional environment variables
+)
+
+
+# Optional: create a sampling callback
+async def handle_sampling_message(
+    message: types.CreateMessageRequestParams,
+) -> types.CreateMessageResult:
+    return types.CreateMessageResult(
+        role="assistant",
+        content=types.TextContent(
+            type="text",
+            text="Hello, world! from model",
+        ),
+        model="gpt-3.5-turbo",
+        stopReason="endTurn",
+    )
+
+
+async def run():
+    async with stdio_client(server_params) as (read, write):
+        async with ClientSession(
+            read, write, sampling_callback=handle_sampling_message
+        ) as session:
+            # Initialize the connection
+            await session.initialize()
+
+            # List available prompts
+            prompts = await session.list_prompts()
+
+            # Get a prompt
+            prompt = await session.get_prompt(
+                "example-prompt", arguments={"arg1": "value"}
+            )
+
+            # List available resources
+            resources = await session.list_resources()
+
+            # List available tools
+            tools = await session.list_tools()
+
+            # Read a resource
+            content, mime_type = await session.read_resource("file://some/path")
+
+            # Call a tool
+            result = await session.call_tool("tool-name", arguments={"arg1": "value"})
+
+
+if __name__ == "__main__":
+    import asyncio
+
+    asyncio.run(run())
+```
+
+### MCP Primitives
+
+The MCP protocol defines three core primitives that servers can implement:
+
+| Primitive | Control               | Description                                         | Example Use                  |
+|-----------|-----------------------|-----------------------------------------------------|------------------------------|
+| Prompts   | User-controlled       | Interactive templates invoked by user choice        | Slash commands, menu options |
+| Resources | Application-controlled| Contextual data managed by the client application   | File contents, API responses |
+| Tools     | Model-controlled      | Functions exposed to the LLM to take actions        | API calls, data updates      |
+
+### Server Capabilities
+
+MCP servers declare capabilities during initialization:
+
+| Capability  | Feature Flag                 | Description                        |
+|-------------|------------------------------|------------------------------------|
+| `prompts`   | `listChanged`                | Prompt template management         |
+| `resources` | `subscribe`<br/>`listChanged`| Resource exposure and updates      |
+| `tools`     | `listChanged`                | Tool discovery and execution       |
+| `logging`   | -                            | Server logging configuration       |
+| `completion`| -                            | Argument completion suggestions    |
+
+## Documentation
+
+- [Model Context Protocol documentation](https://modelcontextprotocol.io)
+- [Model Context Protocol specification](https://spec.modelcontextprotocol.io)
+- [Officially supported servers](https://github.com/modelcontextprotocol/servers)
+
+## Contributing
+
+We are passionate about supporting contributors of all levels of experience and would love to see you get involved in the project. See the [contributing guide](CONTRIBUTING.md) to get started.
+
+## License
+
+This project is licensed under the MIT License - see the LICENSE file for details.

main.py

@@ -0,0 +1,132 @@
+# TutorX MCP Server
+from mcp.server.fastmcp import FastMCP
+import json
+from typing import List, Dict, Any, Optional
+from datetime import datetime
+
+# Create the TutorX MCP server
+mcp = FastMCP("TutorX")
+
+# ------------------ Core Features ------------------
+
+# Adaptive Learning Engine
+@mcp.tool()
+def assess_skill(student_id: str, concept_id: str) -> Dict[str, Any]:
+    """
+    Assess student's skill level on a specific concept
+    
+    Args:
+        student_id: The unique identifier for the student
+        concept_id: The concept to assess
+        
+    Returns:
+        Dictionary containing skill level and recommendations
+    """
+    # Simulated skill assessment
+    return {
+        "student_id": student_id,
+        "concept_id": concept_id,
+        "skill_level": 0.75,
+        "confidence": 0.85,
+        "recommendations": [
+            "Practice more complex problems",
+            "Review related concept: algebra_linear_equations"
+        ],
+        "timestamp": datetime.now().isoformat()
+    }
+
+@mcp.resource("concept-graph://")
+def get_concept_graph() -> Dict[str, Any]:
+    """Get the full knowledge concept graph"""
+    return {
+        "nodes": [
+            {"id": "math_algebra_basics", "name": "Algebra Basics", "difficulty": 1},
+            {"id": "math_algebra_linear_equations", "name": "Linear Equations", "difficulty": 2},
+            {"id": "math_algebra_quadratic_equations", "name": "Quadratic Equations", "difficulty": 3},
+        ],
+        "edges": [
+            {"from": "math_algebra_basics", "to": "math_algebra_linear_equations", "weight": 1.0},
+            {"from": "math_algebra_linear_equations", "to": "math_algebra_quadratic_equations", "weight": 0.8},
+        ]
+    }
+
+@mcp.resource("learning-path://{student_id}")
+def get_learning_path(student_id: str) -> Dict[str, Any]:
+    """Get personalized learning path for a student"""
+    return {
+        "student_id": student_id,
+        "current_concepts": ["math_algebra_linear_equations"],
+        "recommended_next": ["math_algebra_quadratic_equations"],
+        "mastered": ["math_algebra_basics"],
+        "estimated_completion_time": "2 weeks"
+    }
+
+# Assessment Suite
+@mcp.tool()
+def generate_quiz(concept_ids: List[str], difficulty: int = 2) -> Dict[str, Any]:
+    """
+    Generate a quiz based on specified concepts and difficulty
+    
+    Args:
+        concept_ids: List of concept IDs to include in the quiz
+        difficulty: Difficulty level from 1-5
+        
+    Returns:
+        Quiz object with questions and answers
+    """
+    return {
+        "quiz_id": "q12345",
+        "concept_ids": concept_ids,
+        "difficulty": difficulty,
+        "questions": [
+            {
+                "id": "q1",
+                "text": "Solve for x: 2x + 3 = 7",
+                "type": "algebraic_equation",
+                "answer": "x = 2",
+                "solution_steps": [
+                    "2x + 3 = 7",
+                    "2x = 7 - 3",
+                    "2x = 4",
+                    "x = 4/2 = 2"
+                ]
+            }
+        ]
+    }
+
+# Feedback System
+@mcp.tool()
+def analyze_error_patterns(student_id: str, concept_id: str) -> Dict[str, Any]:
+    """
+    Analyze common error patterns for a student on a specific concept
+    
+    Args:
+        student_id: The student's unique identifier
+        concept_id: The concept to analyze
+        
+    Returns:
+        Error pattern analysis
+    """
+    return {
+        "student_id": student_id,
+        "concept_id": concept_id,
+        "common_errors": [
+            {
+                "type": "sign_error",
+                "frequency": 0.65,
+                "example": "2x - 3 = 5 → 2x = 5 - 3 → 2x = 2 → x = 1 (should be x = 4)"
+            },
+            {
+                "type": "arithmetic_error",
+                "frequency": 0.35,
+                "example": "2x = 8 → x = 8/2 = 3 (should be x = 4)"
+            }
+        ],
+        "recommendations": [
+            "Practice more sign manipulation problems",
+            "Review basic arithmetic operations"
+        ]
+    }
+
+if __name__ == "__main__":
+    mcp.run()

pyproject.toml

@@ -0,0 +1,9 @@
+[project]
+name = "tutorx-mcp"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "mcp[cli]>=1.9.3",
+]

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