MCP server for image analysis using OpenRouter
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# MCP OpenVision [](https://github.com/Nazruden/mcp-openvision/actions/workflows/ci.yml) [](https://pypi.org/project/mcp-openvision/) [](https://pypi.org/project/mcp-openvision/) [](https://opensource.org/licenses/MIT) [](https://buymeacoffee.com/nazruden) ## 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 ### Using pip ```bash pip install mcp-openvision ``` ### Using UV (recommended) ```bash uv pip install 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…
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