image_analysis

Analyze images with OpenRouter's vision models by providing a query and optional system prompt. Use this tool for detailed visual insights, object identification, or competitive analysis in AI-driven projects.

Instructions

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."
        )

Input Schema

Name	Required	Default
`frequency_penalty`	No
`image`	Yes
`max_tokens`	No
`model`	No
`presence_penalty`	No
`project_root`	No
`query`	No	Describe this image in detail
`system_prompt`	No	You are an expert vision analyzer with exceptional attention to detail. Your purpose is to provide accurate, comprehensive descriptions of images that help AI agents understand visual content they cannot directly perceive. Focus on describing all relevant elements in the image - objects, people, text, colors, spatial relationships, actions, and context. Be precise but concise, organizing information from most to least important. Avoid making assumptions beyond what's visible and clearly indicate any uncertainty. When text appears in images, transcribe it verbatim within quotes. Respond only with factual descriptions without subjective judgments or creative embellishments. Your descriptions should enable an agent to make informed decisions based solely on your analysis.
`temperature`	No
`top_p`	No

Input Schema (JSON Schema)

{
  "properties": {
    "frequency_penalty": {
      "anyOf": [
        {
          "type": "number"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Frequency Penalty"
    },
    "image": {
      "title": "Image",
      "type": "string"
    },
    "max_tokens": {
      "default": 4000,
      "title": "Max Tokens",
      "type": "integer"
    },
    "model": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Model"
    },
    "presence_penalty": {
      "anyOf": [
        {
          "type": "number"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Presence Penalty"
    },
    "project_root": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Project Root"
    },
    "query": {
      "default": "Describe this image in detail",
      "title": "Query",
      "type": "string"
    },
    "system_prompt": {
      "default": "You are an expert vision analyzer with exceptional attention to detail. Your purpose is to provide accurate, comprehensive descriptions of images that help AI agents understand visual content they cannot directly perceive. Focus on describing all relevant elements in the image - objects, people, text, colors, spatial relationships, actions, and context. Be precise but concise, organizing information from most to least important. Avoid making assumptions beyond what's visible and clearly indicate any uncertainty. When text appears in images, transcribe it verbatim within quotes. Respond only with factual descriptions without subjective judgments or creative embellishments. Your descriptions should enable an agent to make informed decisions based solely on your analysis.",
      "title": "System Prompt",
      "type": "string"
    },
    "temperature": {
      "default": 0.7,
      "title": "Temperature",
      "type": "number"
    },
    "top_p": {
      "anyOf": [
        {
          "type": "number"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Top P"
    }
  },
  "required": [
    "image"
  ],
  "title": "image_analysisArguments",
  "type": "object"
}

mcp-openvision

image_analysis

Instructions

Input Schema

Input Schema (JSON Schema)

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