first commit

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# IDEs
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS generated files
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+Thumbs.db
+
+# Project-specific files
+# Server logs
+*.log
+image_server.log
+
+# Configuration files with sensitive data
+config.json
+secrets.json
+.env.local
+.env.production
+
+# Temporary files
+tmp/
+temp/
+.tmp/
+
+# MCP Server specific
+# Kilocode configuration (may contain API keys)
+kilocode_config.json
+
+# Build artifacts
+*.tar.gz
+*.zip
+*.whl
+
+# Local development files
+run_local.sh
+test_images/
+.local/
+
+# Backup files
+*.bak
+*.backup
+*~.nib
+
+# API keys and secrets (extra safety)
+*api_key*
+*secret*
+*token*
+credentials.json

LICENSE

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+MIT License
+
+Copyright (c) 2025 Image Recognition MCP Server
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

MANIFEST.in

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+include README.md
+include LICENSE
+include requirements.txt
+include run.sh
+recursive-include image_recognition_server *.py

PUBLISHING.md

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+# Publishing Guide
+
+This guide explains how to make the Image Recognition MCP Server portable and distributable.
+
+## Current Setup (Local)
+
+The server currently runs locally using the `run.sh` script. This is configured in Kilocode's MCP settings:
+
+```json
+{
+  "mcpServers": {
+    "image-recognition": {
+      "command": "/home/enne2/Sviluppo/tetris-sdl/mcp-image-server/run.sh",
+      "args": [],
+      "env": {
+        "OPENAI_API_KEY": "your-openai-api-key-here"
+      }
+    }
+  }
+}
+```
+
+## Making It Portable (PyPI Distribution)
+
+To make this server portable and installable by anyone, you can publish it to PyPI.
+
+### Prerequisites
+
+1. Install build tools:
+```bash
+pip install build twine
+```
+
+2. Create a PyPI account at https://pypi.org/account/register/
+
+3. Create an API token at https://pypi.org/manage/account/token/
+
+### Step 1: Update Package Metadata
+
+Edit `setup.py` and `pyproject.toml` to update:
+- `author` and `author_email`
+- `url` (your GitHub repository)
+- Version number
+
+### Step 2: Build the Package
+
+```bash
+cd /home/enne2/Sviluppo/tetris-sdl/mcp-image-server
+python -m build
+```
+
+This creates:
+- `dist/image-recognition-mcp-0.1.0.tar.gz` (source distribution)
+- `dist/image_recognition_mcp-0.1.0-py3-none-any.whl` (wheel distribution)
+
+### Step 3: Test Locally
+
+Before publishing, test the package locally:
+
+```bash
+pip install dist/image_recognition_mcp-0.1.0-py3-none-any.whl
+```
+
+Then test the command:
+```bash
+image-recognition-mcp
+```
+
+### Step 4: Publish to PyPI
+
+#### Option A: Using Twine (Recommended)
+
+```bash
+python -m twine upload dist/*
+```
+
+You'll be prompted for your PyPI username and password/token.
+
+#### Option B: Using UV (Modern Alternative)
+
+```bash
+uv publish
+```
+
+Set your PyPI token:
+```bash
+export UV_PUBLISH_TOKEN="your-pypi-token"
+```
+
+### Step 5: Update Kilocode Configuration
+
+After publishing, users can install and use your server with:
+
+```bash
+pip install image-recognition-mcp
+```
+
+Or use it directly with `uvx` (like `npx` for Python):
+
+```json
+{
+  "mcpServers": {
+    "image-recognition": {
+      "command": "uvx",
+      "args": [
+        "image-recognition-mcp"
+      ],
+      "env": {
+        "OPENAI_API_KEY": "your-openai-api-key-here"
+      }
+    }
+  }
+}
+```
+
+Or with `pipx`:
+
+```json
+{
+  "mcpServers": {
+    "image-recognition": {
+      "command": "pipx",
+      "args": [
+        "run",
+        "image-recognition-mcp"
+      ],
+      "env": {
+        "OPENAI_API_KEY": "your-openai-api-key-here"
+      }
+    }
+  }
+}
+```
+
+## Benefits of PyPI Distribution
+
+1. **Easy Installation**: Users can install with `pip install image-recognition-mcp`
+2. **Version Management**: Easy to update and manage versions
+3. **Dependency Management**: Automatically installs required dependencies
+4. **Portable**: Works on any system with Python installed
+5. **No Local Path**: No need for absolute paths in configuration
+
+## Alternative: GitHub Distribution
+
+If you don't want to publish to PyPI, you can distribute via GitHub:
+
+```json
+{
+  "mcpServers": {
+    "image-recognition": {
+      "command": "uvx",
+      "args": [
+        "--from",
+        "git+https://github.com/yourusername/image-recognition-mcp.git",
+        "image-recognition-mcp"
+      ],
+      "env": {
+        "OPENAI_API_KEY": "your-openai-api-key-here"
+      }
+    }
+  }
+}
+```
+
+## Updating the Package
+
+1. Update version in `setup.py` and `pyproject.toml`
+2. Rebuild: `python -m build`
+3. Republish: `python -m twine upload dist/*`
+
+Users will get updates automatically when they reinstall or when using `uvx`.
+
+## Testing Before Publishing
+
+Always test your package locally before publishing:
+
+```bash
+# Build
+python -m build
+
+# Install locally
+pip install dist/*.whl
+
+# Test
+image-recognition-mcp
+
+# Uninstall
+pip uninstall image-recognition-mcp
+```
+
+## Current Status
+
+✅ Package structure ready
+✅ `setup.py` configured
+✅ `pyproject.toml` configured
+✅ `MANIFEST.in` created
+✅ LICENSE added
+✅ Entry point configured
+
+**Ready to publish!** Just update the metadata and follow the steps above.

README.md

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+# MCP Image Recognition Server
+
+An MCP (Model Context Protocol) server that provides AI-powered image analysis tools for AI assistants.
+
+## Features
+
+- **describe_image**: Analyze images from base64 encoded data using OpenAI's Vision API
+- **describe_image_from_file**: Analyze images from file paths using OpenAI's Vision API
+- Automatic fallback to basic metadata if OpenAI API is not configured
+- **Automatic Kilocode configuration** on installation
+- Portable and distributable via PyPI
+
+## Quick Installation (Recommended)
+
+Install from PyPI (once published):
+
+```bash
+pip install image-recognition-mcp
+```
+
+The server will **automatically configure itself** in Kilocode during installation! 🎉
+
+If automatic configuration doesn't work, you can manually run:
+
+```bash
+image-recognition-mcp-install
+```
+
+## Local Development Setup
+
+For local development or if you want to run from source:
+
+```bash
+cd /home/enne2/Sviluppo/tetris-sdl/mcp-image-server
+./run.sh
+```
+
+The script will automatically:
+- ✅ Create virtual environment if it doesn't exist
+- ✅ Install dependencies if needed
+- ✅ Activate the virtual environment
+- ✅ Start the server
+
+## Configuration
+
+After installation, you need to add your OpenAI API key:
+
+1. Open Kilocode's MCP settings:
+   `~/.config/VSCodium/User/globalStorage/kilocode.kilo-code/settings/mcp_settings.json`
+
+2. Find the `image-recognition` server entry
+
+3. Replace `"your-openai-api-key-here"` with your actual OpenAI API key
+
+4. Restart Kilocode
+
+## Available Tools
+
+### 1. describe_image
+Analyzes an image from base64 encoded data using OpenAI's GPT-4 Vision.
+
+**Parameters:**
+- `image_data` (string, required): Base64 encoded image data
+- `mime_type` (string, optional): MIME type of the image (default: 'image/jpeg')
+
+**Returns:** Detailed AI-generated description of the image including objects, colors, composition, and visible text
+
+**Fallback:** If OpenAI API is not configured, returns basic image metadata (size, mode, format)
+
+### 2. describe_image_from_file
+Analyzes an image from a file path using OpenAI's GPT-4 Vision.
+
+**Parameters:**
+- `file_path` (string, required): Path to the image file
+
+**Returns:** Detailed AI-generated description of the image
+
+**Supported formats:** JPEG, PNG, GIF, WebP (automatically detected from file extension)
+
+## Example Usage
+
+Once configured in Kilocode with a valid OpenAI API key:
+
+```
+Can you analyze the image at /path/to/image.jpg?
+```
+
+The AI will use the `describe_image_from_file` tool to provide a detailed description.
+
+## Installation Methods
+
+### Method 1: PyPI (Recommended - Once Published)
+
+```bash
+pip install image-recognition-mcp
+```
+
+Automatically configures Kilocode! ✨
+
+### Method 2: From Source
+
+```bash
+git clone https://github.com/yourusername/image-recognition-mcp.git
+cd image-recognition-mcp
+pip install -e .
+```
+
+### Method 3: Using uvx (Portable)
+
+```bash
+uvx image-recognition-mcp
+```
+
+No installation needed! Works like `npx` for Python.
+
+## Kilocode Configuration
+
+The server automatically adds this configuration:
+
+```json
+{
+  "mcpServers": {
+    "image-recognition": {
+      "command": "uvx",
+      "args": ["image-recognition-mcp"],
+      "env": {
+        "OPENAI_API_KEY": "your-openai-api-key-here"
+      }
+    }
+  }
+}
+```
+
+## Files Structure
+
+```
+mcp-image-server/
+├── run.sh                          # Local startup script
+├── requirements.txt                # Python dependencies
+├── setup.py                        # Package setup (with auto-config)
+├── pyproject.toml                  # Modern Python packaging
+├── README.md                       # This file
+├── PUBLISHING.md                   # Publishing guide
+├── LICENSE                         # MIT License
+├── MANIFEST.in                     # Package manifest
+├── image_server.log               # Server logs
+├── venv/                          # Virtual environment (auto-created)
+└── image_recognition_server/
+    ├── __init__.py
+    ├── server.py                  # Main server implementation
+    └── install.py                 # Auto-configuration script
+```
+
+## Commands
+
+After installation, these commands are available:
+
+- `image-recognition-mcp` - Start the MCP server
+- `image-recognition-mcp-install` - Configure Kilocode (runs automatically on install)
+
+## Dependencies
+
+- **fastmcp**: FastMCP framework for building MCP servers
+- **pillow**: Python Imaging Library for image processing
+- **openai**: OpenAI API client for Vision API
+
+## Logs
+
+Server logs are written to:
+`/home/enne2/Sviluppo/tetris-sdl/mcp-image-server/image_server.log` (local)
+
+Or when installed via pip:
+`~/.local/share/image-recognition-mcp/logs/` (system-wide)
+
+## How It Works
+
+1. **With OpenAI API Key:**
+   - Images are encoded to base64
+   - Sent to OpenAI's GPT-4o-mini Vision model
+   - Returns detailed AI-generated descriptions
+
+2. **Without OpenAI API Key:**
+   - Falls back to basic image metadata
+   - Returns size, color mode, and format information
+   - Includes a note about configuring the API key
+
+## Troubleshooting
+
+### Server won't start
+- Check that Python 3.8+ is installed: `python3 --version`
+- Verify installation: `pip show image-recognition-mcp`
+- Check logs for errors
+
+### Automatic configuration failed
+- Run manually: `image-recognition-mcp-install`
+- Or configure manually (see PUBLISHING.md)
+
+### No AI descriptions
+- Verify your OpenAI API key is correctly set in MCP settings
+- Check that the key is valid and has credits
+- Review logs for API errors
+- The server will show a warning on startup if no valid API key is detected
+
+### Image not found
+- Ensure the file path is absolute
+- Check file permissions
+- Verify the file exists: `ls -la /path/to/image.jpg`
+
+## Development
+
+To modify the server:
+
+1. Clone the repository
+2. Install in development mode: `pip install -e .`
+3. Make changes to `image_recognition_server/server.py`
+4. Test locally: `image-recognition-mcp`
+
+## Publishing
+
+See [PUBLISHING.md](PUBLISHING.md) for instructions on publishing to PyPI.
+
+## License
+
+MIT License - see [LICENSE](LICENSE) file for details.
+
+## Contributing
+
+Contributions are welcome! Please feel free to submit a Pull Request.
+
+## Future Enhancements
+
+- Support for batch image processing
+- Image comparison tools
+- Custom vision models
+- Image generation capabilities
+- Support for more image formats
+- Caching for repeated image analyses
+- Web interface for testing

image_recognition_server/__init__.py

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+# MCP Image Recognition Server Package
+from .server import mcp

image_recognition_server/install.py

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+#!/usr/bin/env python3
+"""
+Post-installation script to automatically configure Kilocode MCP settings.
+"""
+
+import json
+import os
+import sys
+from pathlib import Path
+
+
+def get_kilocode_config_path():
+    """Find the Kilocode MCP settings file."""
+    # Common paths for Kilocode/VSCodium
+    possible_paths = [
+        Path.home() / ".config/VSCodium/User/globalStorage/kilocode.kilo-code/settings/mcp_settings.json",
+        Path.home() / ".config/Code/User/globalStorage/kilocode.kilo-code/settings/mcp_settings.json",
+        Path.home() / "Library/Application Support/VSCodium/User/globalStorage/kilocode.kilo-code/settings/mcp_settings.json",
+        Path.home() / "Library/Application Support/Code/User/globalStorage/kilocode.kilo-code/settings/mcp_settings.json",
+    ]
+    
+    for path in possible_paths:
+        if path.exists():
+            return path
+    
+    return None
+
+
+def add_to_kilocode_config():
+    """Add the image-recognition server to Kilocode's MCP settings."""
+    config_path = get_kilocode_config_path()
+    
+    if not config_path:
+        print("⚠️  Kilocode MCP settings file not found.")
+        print("   You'll need to manually add the server configuration.")
+        print("\n   Add this to your MCP settings:")
+        print(json.dumps({
+            "image-recognition": {
+                "command": "uvx",
+                "args": ["image-recognition-mcp"],
+                "env": {
+                    "OPENAI_API_KEY": "your-openai-api-key-here"
+                }
+            }
+        }, indent=2))
+        return False
+    
+    try:
+        # Read existing config
+        with open(config_path, 'r') as f:
+            config = json.load(f)
+        
+        # Ensure mcpServers exists
+        if "mcpServers" not in config:
+            config["mcpServers"] = {}
+        
+        # Check if already configured
+        if "image-recognition" in config["mcpServers"]:
+            print("✅ Image Recognition MCP server already configured in Kilocode")
+            return True
+        
+        # Add our server
+        config["mcpServers"]["image-recognition"] = {
+            "command": "uvx",
+            "args": ["image-recognition-mcp"],
+            "env": {
+                "OPENAI_API_KEY": "your-openai-api-key-here"
+            }
+        }
+        
+        # Backup original config
+        backup_path = config_path.with_suffix('.json.backup')
+        with open(backup_path, 'w') as f:
+            json.dump(config, f, indent=2)
+        
+        # Write updated config
+        with open(config_path, 'w') as f:
+            json.dump(config, f, indent=2)
+        
+        print("✅ Successfully added Image Recognition MCP server to Kilocode!")
+        print(f"   Config file: {config_path}")
+        print(f"   Backup saved: {backup_path}")
+        print("\n⚠️  Remember to:")
+        print("   1. Replace 'your-openai-api-key-here' with your actual OpenAI API key")
+        print("   2. Restart Kilocode to load the new server")
+        
+        return True
+        
+    except Exception as e:
+        print(f"❌ Error updating Kilocode config: {e}")
+        return False
+
+
+def main():
+    """Main entry point for the installer."""
+    print("🚀 Image Recognition MCP Server - Post-Install Configuration")
+    print("=" * 60)
+    
+    if add_to_kilocode_config():
+        print("\n✨ Installation complete!")
+    else:
+        print("\n⚠️  Manual configuration required.")
+        print("   See PUBLISHING.md for instructions.")
+    
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

image_recognition_server/server.py

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+import base64
+import io
+import logging
+import os
+from typing import Optional
+
+from PIL import Image
+from fastmcp import FastMCP
+import openai
+
+# Configure logging
+logging.basicConfig(
+    level=logging.DEBUG,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    filename='/home/enne2/Sviluppo/tetris-sdl/mcp-image-server/image_server.log'
+)
+logger = logging.getLogger(__name__)
+
+# Create FastMCP server instance
+mcp = FastMCP("ImageRecognitionServer")
+
+# Get OpenAI API key from environment
+OPENAI_API_KEY = os.getenv('OPENAI_API_KEY', '')
+HAS_OPENAI = bool(OPENAI_API_KEY and OPENAI_API_KEY != 'your-openai-api-key-here')
+
+if HAS_OPENAI:
+    openai.api_key = OPENAI_API_KEY
+    logger.info("OpenAI API key configured - AI descriptions enabled")
+else:
+    logger.warning("No valid OpenAI API key - using basic image metadata only")
+
+@mcp.tool()
+def describe_image(image_data: str, mime_type: str = 'image/jpeg') -> str:
+    """
+    Describe an image using base64 encoded image data
+    
+    Args:
+        image_data: Base64 encoded image data
+        mime_type: MIME type of the image (default: image/jpeg)
+    
+    Returns:
+        Detailed description of the image
+    """
+    try:
+        logger.debug(f"Describing image - MIME type: {mime_type}")
+        
+        # Decode base64 image
+        image_bytes = base64.b64decode(image_data)
+        image = Image.open(io.BytesIO(image_bytes))
+        
+        # Log image details
+        logger.info(f"Image size: {image.size}, mode: {image.mode}")
+        
+        # If OpenAI is available, use Vision API
+        if HAS_OPENAI:
+            try:
+                response = openai.chat.completions.create(
+                    model="gpt-4o-mini",
+                    messages=[
+                        {
+                            "role": "user",
+                            "content": [
+                                {"type": "text", "text": "Describe this image in detail, including objects, colors, composition, and any text visible."},
+                                {
+                                    "type": "image_url",
+                                    "image_url": {
+                                        "url": f"data:{mime_type};base64,{image_data}"
+                                    }
+                                }
+                            ]
+                        }
+                    ],
+                    max_tokens=500
+                )
+                
+                description = response.choices[0].message.content
+                logger.debug(f"OpenAI description: {description}")
+                return description
+                
+            except Exception as e:
+                logger.error(f"OpenAI API error: {str(e)}", exc_info=True)
+                # Fall back to basic metadata
+                return f"OpenAI API error: {str(e)}\n\nBasic metadata:\n- Size: {image.size[0]}x{image.size[1]} pixels\n- Mode: {image.mode}\n- Format: {image.format or 'Unknown'}"
+        
+        # Return basic metadata if no OpenAI
+        description = f"Image Analysis (Basic Metadata):\n- Size: {image.size[0]}x{image.size[1]} pixels\n- Mode: {image.mode}\n- Format: {image.format or 'Unknown'}\n\nNote: For AI-powered descriptions, configure OPENAI_API_KEY in MCP settings."
+        logger.debug(f"Returning basic description: {description}")
+        
+        return description
+    
+    except Exception as e:
+        logger.error(f"Error describing image: {str(e)}", exc_info=True)
+        return f"Error describing image: {str(e)}"
+
+@mcp.tool()
+def describe_image_from_file(file_path: str) -> str:
+    """
+    Describe an image from a file path
+    
+    Args:
+        file_path: Path to the image file
+    
+    Returns:
+        Detailed description of the image
+    """
+    try:
+        logger.debug(f"Describing image from file: {file_path}")
+        
+        # Open the image file
+        with open(file_path, 'rb') as image_file:
+            # Encode image to base64
+            image_data = base64.b64encode(image_file.read()).decode('utf-8')
+        
+        # Determine MIME type from file extension
+        mime_type = 'image/jpeg'
+        if file_path.lower().endswith('.png'):
+            mime_type = 'image/png'
+        elif file_path.lower().endswith('.gif'):
+            mime_type = 'image/gif'
+        elif file_path.lower().endswith('.webp'):
+            mime_type = 'image/webp'
+        
+        # Use the describe_image function
+        return describe_image(image_data, mime_type)
+    
+    except Exception as e:
+        logger.error(f"Error reading image file: {str(e)}", exc_info=True)
+        return f"Error reading image file: {str(e)}"
+
+def main():
+    """Main entry point for the MCP server."""
+    logger.info("Starting MCP Image Recognition Server")
+    mcp.run()
+
+if __name__ == "__main__":
+    main()

pyproject.toml

@@ -0,0 +1,45 @@
+[build-system]
+requires = ["setuptools>=61.0", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "image-recognition-mcp"
+version = "0.1.0"
+description = "An MCP server for AI-powered image analysis using OpenAI Vision API"
+readme = "README.md"
+requires-python = ">=3.8"
+license = {text = "MIT"}
+keywords = ["mcp", "modelcontextprotocol", "image", "vision", "openai", "ai"]
+authors = [
+    {name = "Your Name", email = "your.email@example.com"}
+]
+classifiers = [
+    "Development Status :: 3 - Alpha",
+    "Intended Audience :: Developers",
+    "Topic :: Software Development :: Libraries :: Python Modules",
+    "License :: OSI Approved :: MIT License",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+]
+dependencies = [
+    "fastmcp>=2.0.0",
+    "pillow>=10.0.0",
+    "openai>=1.0.0",
+]
+
+[project.urls]
+Homepage = "https://github.com/yourusername/image-recognition-mcp"
+Repository = "https://github.com/yourusername/image-recognition-mcp"
+Issues = "https://github.com/yourusername/image-recognition-mcp/issues"
+
+[project.scripts]
+image-recognition-mcp = "image_recognition_server.server:main"
+image-recognition-mcp-install = "image_recognition_server.install:main"
+
+[tool.setuptools.packages.find]
+where = ["."]
+include = ["image_recognition_server*"]

requirements.txt

@@ -0,0 +1,5 @@
+fastmcp
+pillow
+python-multipart
+uvicorn
+openai

run.sh

@@ -0,0 +1,47 @@
+#!/bin/bash
+
+# MCP Image Recognition Server Startup Script
+# This script sets up the environment and starts the server
+
+set -e  # Exit on error
+
+# Get the directory where this script is located
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+cd "$SCRIPT_DIR"
+
+echo "🚀 Starting MCP Image Recognition Server..."
+echo "📁 Working directory: $SCRIPT_DIR"
+
+# Check if virtual environment exists
+if [ ! -d "venv" ]; then
+    echo "📦 Virtual environment not found. Creating..."
+    python3 -m venv venv
+    echo "✅ Virtual environment created"
+fi
+
+# Activate virtual environment
+echo "🔧 Activating virtual environment..."
+source venv/bin/activate
+
+# Check if dependencies are installed
+if ! python -c "import fastmcp" 2>/dev/null; then
+    echo "📥 Installing dependencies..."
+    pip install -q -r requirements.txt
+    echo "✅ Dependencies installed"
+else
+    echo "✅ Dependencies already installed"
+fi
+
+# Check for OpenAI API key
+if [ -z "$OPENAI_API_KEY" ] || [ "$OPENAI_API_KEY" = "your-openai-api-key-here" ]; then
+    echo "⚠️  Warning: OPENAI_API_KEY not set or using placeholder"
+    echo "   Server will run with basic image metadata only"
+    echo "   To enable AI descriptions, set OPENAI_API_KEY environment variable"
+else
+    echo "✅ OpenAI API key configured"
+fi
+
+# Start the server
+echo "🎯 Starting server..."
+echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
+python -m image_recognition_server.server

setup.py

@@ -0,0 +1,60 @@
+from setuptools import setup, find_packages
+from setuptools.command.install import install
+import subprocess
+import sys
+
+with open("README.md", "r", encoding="utf-8") as fh:
+    long_description = fh.read()
+
+
+class PostInstallCommand(install):
+    """Post-installation for installation mode."""
+    def run(self):
+        install.run(self)
+        # Run the configuration script
+        try:
+            subprocess.check_call([sys.executable, "-m", "image_recognition_server.install"])
+        except Exception as e:
+            print(f"Note: Automatic configuration failed: {e}")
+            print("You can manually configure by running: image-recognition-mcp-install")
+
+
+setup(
+    name="image-recognition-mcp",
+    version="0.1.0",
+    author="Your Name",
+    author_email="your.email@example.com",
+    description="An MCP server for AI-powered image analysis using OpenAI Vision API",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    url="https://github.com/yourusername/image-recognition-mcp",
+    packages=find_packages(),
+    classifiers=[
+        "Development Status :: 3 - Alpha",
+        "Intended Audience :: Developers",
+        "Topic :: Software Development :: Libraries :: Python Modules",
+        "License :: OSI Approved :: MIT License",
+        "Programming Language :: Python :: 3",
+        "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
+        "Programming Language :: Python :: 3.11",
+        "Programming Language :: Python :: 3.12",
+    ],
+    python_requires=">=3.8",
+    install_requires=[
+        "fastmcp>=2.0.0",
+        "pillow>=10.0.0",
+        "openai>=1.0.0",
+    ],
+    entry_points={
+        "console_scripts": [
+            "image-recognition-mcp=image_recognition_server.server:main",
+            "image-recognition-mcp-install=image_recognition_server.install:main",
+        ],
+    },
+    cmdclass={
+        'install': PostInstallCommand,
+    },
+    keywords=["mcp", "modelcontextprotocol", "image", "vision", "openai", "ai"],
+)