PROMPT_ANALYZE_DEFAULT = "Please identify obstacles in the image and estimate the distance to the obstacles."
@mcp.tool(annotations={"readOnlyHint": True})
async def get_or_analyze_image(
    format: str = 'jpeg', jpeg_quality: int = 85, scale: float = 1.0,
    analyze_prompt: str = PROMPT_ANALYZE_DEFAULT,
    ctx: Context = CurrentContext()
) -> str:
    """
    Get or analysis the latest color image from the drone's camera, return base64-encoded image data or analysis report
    Args:
        format: Image format, default is 'jpeg', supports 'jpeg', 'jpg', 'png'
        jpeg_quality: JPEG quality, default is 85, valid range 0-100, and for format is 'jpeg' or 'jpg'
        scale: Image scaling ratio, default is 1.0, valid range 0.1 to 4.0
        analyze_prompt: If set '' or None, just return base64 encoded image uri, default is analyzing image using MCP sampling mechanism
    """
    image_data = tools_executor.execute_tool("get_image", {"format": format, "jpeg_quality": jpeg_quality, "scale": scale})
    if isinstance(image_data, str):
        return image_data
    
    if analyze_prompt:
        # If analyze_flag is True, perform image analysis using the MCP sampling mechanism
        image_content = ImageContent(
            type = "image",
            data = image_data["data_str"],
            mimeType = f"image/{image_data['format']}"
        )
        messages = SamplingMessage(
            role="user",
            content=[
                TextContent(type="text", text=analyze_prompt),
                image_content
            ]
        )

        result = await ctx.sample(
            messages=[messages],
            system_prompt="You are an image analysis expert."
        )

        return result.text if result else "No analysis result"

    return f"data:image/{image_data['format']};base64,{image_data['data_str']}"

from fastmcp import Client
from fastmcp.client.sampling.handlers.openai import OpenAISamplingHandler

mcp_client = Client(
      transport,
      sampling_handler=OpenAISamplingHandler(
            default_model=model,
            client=AsyncOpenAI(api_key=api_key, base_url=base_url)
       )
 )