mseep-mcp-server-gemini-image-analyzer 0.1.1

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MCP-Image-Analysis4Puzzle

A specialized MCP server that uses Gemini 2.5 Pro to analyze and solve mathematical puzzles for children's education.

Overview

MCP-Image-Analysis4Puzzle is a dedicated server that helps teachers, parents, and students analyze mathematical puzzles through image processing. Using Google's Gemini 2.5 Pro model, it provides detailed, grade-appropriate analysis and solutions for various types of mathematical puzzles.

Key Features

Mathematical Subject Analysis

• Number Sense & Operations (counting, arithmetic, fractions)
• Geometry & Spatial Reasoning (shapes, patterns, transformations)
• Algebra & Early Functions (sequences, patterns, simple equations)
• Measurement & Data (time, money, graphs)
• Logic & Problem Solving (visual puzzles, word problems)

Educational Support

• Grade-level appropriate analysis (K-6)
• Common Core Standards alignment
• Step-by-step solution guidance
• Visual learning aids suggestions
• Extension activities

Smart Validation

• Automatic puzzle type detection
• Grade-level appropriateness check
• Mathematical content verification
• Learning objective identification

Requirements

• Python 3.11 or higher
• Google Gemini API key
• MCP-compatible client (Cursor, Claude Desktop, etc.)
• Internet connection for API access

Installation

1. Clone the repository:

git clone https://github.com/your-username/mcp-image-analysis4puzzle.git
cd mcp-image-analysis4puzzle

2. Set up a virtual environment:

# Using venv
python -m venv .venv
source .venv/bin/activate  # On Unix/macOS
.venv\Scripts\activate     # On Windows

# Or using uv (recommended)
uv venv
source .venv/bin/activate  # On Unix/macOS
.venv\Scripts\activate     # On Windows

3. Install dependencies:

# Using pip
pip install -r requirements.txt

# Or using uv (recommended)
uv pip install -r requirements.txt

4. Create and configure your environment file:

cp .env.example .env

5. Add your Gemini API key to .env:

GEMINI_API_KEY=your_api_key_here

Configuration

For Cursor IDE

The server is automatically configured when using Cursor IDE.

For Claude Desktop

Add to your claude_desktop_config.json:

• Windows: %APPDATA%\Claude\claude_desktop_config.json
• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

{
    "mcpServers": {
        "mcp-image-analysis4puzzle": {
            "command": "uv",
            "args": [
                "--directory",
                "/path/to/mcp-image-analysis4puzzle",
                "run",
                "server.py"
            ],
            "env": {
                "GEMINI_API_KEY": "your_api_key_here"
            }
        }
    }
}

Usage

1. Start your MCP-compatible client (Cursor or Claude Desktop)
2. Upload a mathematical puzzle image
3. Ask for analysis using commands like:
   • "Analyze this math puzzle for grade 2"
   • "Help solve this geometry puzzle"
   • "What math concepts are in this puzzle?"

Example Analysis

When you upload a puzzle image, you'll receive:

PUZZLE ANALYSIS

Subject: Number Sense & Operations
Grade Level: 2nd Grade (7-8 years)
Topic: Skip Counting & Patterns

Mathematical Concepts:
- Pattern recognition
- Skip counting by 2s
- Number relationships
- Early multiplication concepts

Step-by-Step Solution:
1. Observe the number sequence
2. Identify the pattern
3. Apply the pattern rule
4. Verify the answer

Learning Standards:
- CCSS.MATH.CONTENT.2.OA.C.3
- CCSS.MATH.PRACTICE.MP7
- CCSS.MATH.PRACTICE.MP8

Visual Aids:
- Number line
- Counting objects
- Pattern blocks
- Drawing tools

Extension Activities:
1. Create similar patterns
2. Find patterns in real life
3. Connect to multiplication
4. Practice with different numbers

Development

To run the server in development mode:

fastmcp dev server.py

This starts the server and makes the MCP Inspector available at http://localhost:5173

Project Structure

mcp-image-analysis4puzzle/
├── server.py           # Main MCP server implementation
├── prompts.py         # Gemini prompt templates
├── utils.py           # Utility functions
├── requirements.txt   # Python dependencies
├── .env              # Environment configuration
└── README.md         # Documentation

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

MIT License

Acknowledgments

• Google Gemini API
• FastMCP Framework
• Claude AI Platform
• Cursor IDE Team