wojons-mcp-openvision 0.6.3
MCP server for image analysis using OpenRouter
License: MIT
Requires Python: >=3.10

# MCP OpenVision

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## Overview

MCP OpenVision is a Model Context Protocol (MCP) server that provides image analysis capabilities powered by OpenRouter vision models. It enables AI assistants to analyze images via a simple interface within the MCP ecosystem.

## Installation

### Installing via Smithery

To install mcp-openvision for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@Nazruden/mcp-openvision):

```bash
npx -y @smithery/cli install @Nazruden/mcp-openvision --client claude
```

### Using pip

```bash
pip install mcp-openvision
```

### Using UV (recommended)

```bash
uv pip install mcp-openvision
```

### Installing this fork (PyPI)

This repository is a maintained fork published under the distribution name "wojons-mcp-openvision".

```bash
pip install wojons-mcp-openvision
# or
uv pip install wojons-mcp-openvision
```

## Configuration

MCP OpenVision requires an OpenRouter API key and can be configured through environment variables:

- **OPENROUTER_API_KEY** (required): Your OpenRouter API key
- **OPENROUTER_DEFAULT_MODEL** (optional): The vision model to use

### OpenRouter Vision Models

MCP OpenVision works with any OpenRouter model that supports vision capabilities. The default model is `qwen/qwen2.5-vl-32b-instruct:free`, but you can specify any other compatible model.

Some popular vision models available through OpenRouter include:

- `qwen/qwen2.5-vl-32b-instruct:free` (default)
- `anthropic/claude-3-5-sonnet`
- `anthropic/claude-3-opus`
- `anthropic/claude-3-sonnet`
- `openai/gpt-4o`

You can specify custom models by setting the `OPENROUTER_DEFAULT_MODEL` environment variable or by passing the `model` parameter directly to the `image_analysis` function.

## Usage

### Testing with MCP Inspector

The easiest way to test MCP OpenVision is with the MCP Inspector tool:

```bash
npx @modelcontextprotocol/inspector uvx --from wojons-mcp-openvision mcp-openvision
```

### Integration with Claude Desktop or Cursor

1. Edit your MCP configuration file:

   - Windows: `%USERPROFILE%\.cursor\mcp.json`
   - macOS: `~/.cursor/mcp.json` or `~/Library/Application Support/Claude/claude_desktop_config.json`

2. Add the following configuration:

```json
{
  "mcpServers": {
    "openvision": {
      "command": "uvx",
      "args": ["--from", "wojons-mcp-openvision", "mcp-openvision"],
      "env": {
        "OPENROUTER_API_KEY": "your_openrouter_api_key_here",
        "OPENROUTER_DEFAULT_MODEL": "anthropic/claude-3-sonnet"
      }
    }
  }
}
```

### Running Locally for Development

```bash
# Set the required API key
export OPENROUTER_API_KEY="your_api_key"

# Run the server module directly
python -m mcp_openvision
```

## Features

MCP OpenVision provides the following core tool:

- **image_analysis**: Analyze images with vision models, supporting various parameters:
  - `image`: Can be provided as:
    - Base64-encoded image data
    - Image URL (http/https)
    - Local file path
  - `query`: User instruction for the image analysis task
  - `system_prompt`: Instructions that define the model's role and behavior (optional)
  - `model`: Vision model to use
  - `temperature`: Controls randomness (0.0-1.0)
  - `max_tokens`: Maximum response length

### Crafting Effective Queries

The `query` parameter is crucial for getting useful results from the image analysis. A well-crafted query provides context about:

1. **Purpose**: Why you're analyzing this image
2. **Focus areas**: Specific elements or details to pay attention to
3. **Required information**: The type of information you need to extract
4. **Format preferences**: How you want the results structured

#### Examples of Effective Queries

| Basic Query             | Enhanced Query                                                                                                       |
| ----------------------- | -------------------------------------------------------------------------------------------------------------------- |
| "Describe this image"   | "Identify all retail products visible in this store shelf image and estimate their price range"                      |
| "What's in this image?" | "Analyze this medical scan for abnormalities, focusing on the highlighted area and providing possible diagnoses"     |
| "Analyze this chart"    | "Extract the numerical data from this bar chart showing quarterly sales, and identify the key trends from 2022-2023" |
| "Read the text"         | "Transcribe all visible text in this restaurant menu, preserving the item names, descriptions, and prices"           |

By providing context about why you need the analysis and what specific information you're seeking, you help the model focus on relevant details and produce more valuable insights.

### Example Usage

```python
# Analyze an image from a URL
result = await image_analysis(
    image="https://example.com/image.jpg",
    query="Describe this image in detail"
)

# Analyze an image from a local file with a focused query
result = await image_analysis(
    image="path/to/local/image.jpg",
    query="Identify all traffic signs in this street scene and explain their meanings for a driver education course"
)

# Analyze with a base64-encoded image and a specific analytical purpose
result = await image_analysis(
    image="SGVsbG8gV29ybGQ=...",  # base64 data
    query="Examine this product packaging design and highlight elements that could be improved for better visibility and brand recognition"
)

# Customize the system prompt for specialized analysis
result = await image_analysis(
    image="path/to/local/image.jpg",
    query="Analyze the composition and artistic techniques used in this painting, focusing on how they create emotional impact",
    system_prompt="You are an expert art historian with deep knowledge of painting techniques and art movements. Focus on formal analysis of composition, color, brushwork, and stylistic elements."
)
```

## Image Input Types

The `image_analysis` tool accepts several types of image inputs:

1. **Base64-encoded strings**
2. **Image URLs** - must start with http:// or https://
3. **File paths**:
   - **Absolute paths**: full paths starting with / (Unix) or drive letter (Windows)
   - **Relative paths**: paths relative to the current working directory
   - **Relative paths with project_root**: use the `project_root` parameter to specify a base directory

### Using Relative Paths

When using relative file paths (like "examples/image.jpg"), you have two options:

1. The path must be relative to the current working directory where the server is running
2. Or, you can specify a `project_root` parameter:

```python
# Example with relative path and project_root
result = await image_analysis(
    image="examples/image.jpg",
    project_root="/path/to/your/project",
    query="What is in this image?"
)
```

This is particularly useful in applications where the current working directory may not be predictable or when you want to reference files using paths relative to a specific directory.

## Development

### Setup Development Environment

```bash
# Clone the repository
git clone https://github.com/wojons/mcp-openvision.git
cd mcp-openvision

# Install development dependencies
pip install -e ".[dev]"
```

### Code Formatting

This project uses Black for automatic code formatting. The formatting is enforced through GitHub Actions:

- All code pushed to the repository is automatically formatted with Black
- For pull requests from repository collaborators, Black formats the code and commits directly to the PR branch
- For pull requests from forks, Black creates a new PR with the formatted code that can be merged into the original PR

You can also run Black locally to format your code before committing:

```bash
# Format all Python code in the src and tests directories
black src tests
```

### Run Tests

```bash
pytest
```

### Release Process

This project uses an automated release process:

1. Update the version in `pyproject.toml` following [Semantic Versioning](https://semver.org/) principles
   - You can use the helper script: `python scripts/bump_version.py [major|minor|patch]`
2. Update the `CHANGELOG.md` with details about the new version
   - The script also creates a template entry in CHANGELOG.md that you can fill in
3. Commit and push these changes to the `main` branch
4. The GitHub Actions workflow will:
   - Detect the version change
   - Automatically create a new GitHub release
   - Trigger the publishing workflow that publishes to PyPI

This automation helps maintain a consistent release process and ensures that every release is properly versioned and documented.

## Support

If you find this project helpful, consider buying me a coffee to support ongoing development and maintenance.

<a href="https://www.buymeacoffee.com/nazruden" target="_blank">
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## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.