"""Image generation and editing tools for NanoBanana API."""
from typing import Annotated
from pydantic import Field
from core.client import client
from core.server import mcp
from core.utils import format_image_result
@mcp.tool()
async def nanobanana_generate_image(
prompt: Annotated[
str,
Field(
description="Description of the image to generate. Be descriptive about subject, atmosphere, lighting, camera/lens, and quality. Example: 'A photorealistic close-up portrait of an elderly Japanese ceramicist with deep wrinkles and a warm smile, soft golden hour light, 85mm portrait lens, bokeh background'"
),
],
callback_url: Annotated[
str,
Field(
description="Optional webhook URL to receive the result asynchronously. The API will POST the result to this URL when complete."
),
] = "",
) -> str:
"""Generate an AI image from a text prompt using Google's Nano Banana model.
This creates high-quality images from detailed text descriptions. The more
descriptive your prompt, the better the results.
Use this when:
- You want to generate a new image from scratch
- You have a detailed description of the desired image
- You need photorealistic or artistic image generation
Prompt writing tips:
- Include: Main subject + Atmosphere + Lighting + Camera/Lens + Quality keywords
- Example: "Urban career woman, backlit sunlight, film grain, orange-gold tones, hopeful dawn"
Returns:
Task ID, trace ID, and generated image URL.
"""
payload: dict = {
"action": "generate",
"prompt": prompt,
}
if callback_url:
payload["callback_url"] = callback_url
result = await client.generate_image(**payload)
return format_image_result(result)
@mcp.tool()
async def nanobanana_edit_image(
prompt: Annotated[
str,
Field(
description="Description of the edit to perform. Describe how the images should be combined or modified. Example: 'let this person wear this T-shirt', 'place this product in a modern kitchen scene'"
),
],
image_urls: Annotated[
list[str],
Field(
description="List of image URLs to edit. Can be HTTP/HTTPS URLs (publicly accessible) or Base64-encoded images (data:image/png;base64,...). When combining multiple images, describe their relationship in the prompt."
),
],
callback_url: Annotated[
str,
Field(
description="Optional webhook URL to receive the result asynchronously. The API will POST the result to this URL when complete."
),
] = "",
) -> str:
"""Edit or combine images using AI based on a text prompt.
This allows you to modify existing images or combine multiple images together.
Perfect for virtual try-on, product placement, image enhancement, and more.
Use this when:
- You want to combine multiple images (e.g., person + clothing)
- You want to modify an existing image
- You need virtual try-on (putting clothes on a person)
- You want to place products in different scenes
- You need to change attributes (materials, colors, styles)
Common use cases:
- Portrait replacement: Try different clothing on same person
- Product scene composition: Place products in realistic environments
- Attribute replacement: Change materials, colors, or variants
- Poster editing: Rapidly change styles or themes
- 2D to 3D conversion: Convert images to 3D product mockups
- Image restoration: Restore old or damaged photos
Returns:
Task ID, trace ID, and edited image URL.
"""
payload: dict = {
"action": "edit",
"prompt": prompt,
"image_urls": image_urls,
}
if callback_url:
payload["callback_url"] = callback_url
result = await client.edit_image(**payload)
return format_image_result(result)
