Nanana AI Image Generation Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| NANANA_API_URL | No | Custom API URL (defaults to https://nanana.app) | https://nanana.app |
| NANANA_API_TOKEN | Yes | Your Nanana AI API token |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| text_to_imageA | Generate an image from a text prompt using Nanana AI. This operation typically takes 15-30 seconds to complete. The tool will wait for generation to finish and return the final image URL. |
| image_to_imageA | Transform existing images based on a text prompt using Nanana AI. This operation typically takes 15-30 seconds to complete. The tool will wait for transformation to finish and return the final image URL. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: image_to_image transforms existing images based on text prompts, while text_to_image generates new images from text prompts. There is no overlap or ambiguity between these operations.
Both tools follow a perfect verb_noun pattern (image_to_image, text_to_image) with consistent snake_case formatting. The naming convention is predictable and readable throughout the set.
With only 2 tools for an AI image generation server, the surface feels thin and incomplete. While the two core operations are covered, typical image generation servers would include additional functionality like parameter controls, style options, batch operations, or image editing capabilities.
The server covers the basic text-to-image and image-to-image transformations but lacks essential functionality for a complete image generation workflow. There are no tools for controlling generation parameters (like resolution, style, seed values), managing generated images, editing existing images beyond transformations, or handling batch operations.