Nazruden/mcp-openvision
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MCP OpenVision is a Model Context Protocol server for image analysis using OpenRouter vision models.
Tools
Functions exposed to the LLM to take actions
image_analysis
Analyze an image using OpenRouter's vision capabilities.
This tool allows you to send an image to OpenRouter's vision models for analysis. You provide a query to guide the analysis and can optionally customize the system prompt for more control over the model's behavior.
Args: image: The image as a base64-encoded string, URL, or local file path query: Text prompt to guide the image analysis. For best results, provide context about why you're analyzing the image and what specific information you need. Including details about your purpose and required focus areas leads to more relevant and useful responses. system_prompt: Instructions for the model defining its role and behavior model: The vision model to use (defaults to the value set by OPENROUTER_DEFAULT_MODEL) max_tokens: Maximum number of tokens in the response (100-4000) temperature: Temperature parameter for generation (0.0-1.0) top_p: Optional nucleus sampling parameter (0.0-1.0) presence_penalty: Optional penalty for new tokens based on presence in text so far (0.0-2.0) frequency_penalty: Optional penalty for new tokens based on frequency in text so far (0.0-2.0) project_root: Optional root directory to resolve relative image paths against
Returns: The analysis result as text
Examples: Basic usage with a file path: image_analysis(image="path/to/image.jpg", query="Describe this image in detail")
Basic usage with an image URL:
image_analysis(image="https://example.com/image.jpg", query="Describe this image in detail")
Basic usage with a relative path and project root:
image_analysis(image="examples/image.jpg", project_root="/path/to/project", query="Describe this image in detail")
Usage with a detailed contextual query:
image_analysis(
image="path/to/image.jpg",
query="Analyze this product packaging design for a fitness supplement. Identify all nutritional claims,
certifications, and health icons. Assess the visual hierarchy and how the key selling points
are communicated. This is for a competitive analysis project."
)
Usage with custom system prompt:
image_analysis(
image="path/to/image.jpg",
query="What objects can you see in this image?",
system_prompt="You are an expert at identifying objects in images. Focus on listing all visible objects."
)
Prompts
Interactive templates invoked by user choice
No prompts
Resources
Contextual data attached and managed by the client
