Animagine MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
| logging | {} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| extensions | {
"io.modelcontextprotocol/ui": {}
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| validate_promptA | Validate a prompt against Animagine XL rules. Checks for:
|
| optimize_promptA | Optimize a prompt for Animagine XL. Provide either a natural language description or an existing prompt. The optimizer will:
|
| explain_promptA | Explain what each tag in a prompt does. Breaks down the prompt into individual tags with:
|
| list_modelsA | List available checkpoints and LoRAs for image generation. Returns all available models with metadata:
Use this to discover what models are available before generation. Returns: Dictionary with checkpoints, loras, default_checkpoint, and currently_loaded |
| load_checkpointA | Pre-load a checkpoint into GPU memory. Loading a checkpoint in advance speeds up subsequent generation calls. Use list_models() to see available checkpoints. |
| unload_lorasA | Unload all LoRA weights from the current pipeline. Useful to reset to base checkpoint style without reloading the full model. This is faster than reloading the checkpoint. Returns: Status with success, unloaded_count, and message |
| generate_imageA | Generate an image with Animagine XL 4.0. Uses the Diffusers pipeline with the lpw_stable_diffusion_xl custom pipeline. Images are saved to outputs/YYYY-MM-DD/ with accompanying metadata JSON. Supports custom checkpoints and LoRA mixing for style control. Recommended workflow:
|
| generate_image_from_imageA | Generate an image using img2img (image-to-image) transformation. Takes an existing image and transforms it based on the prompt while preserving structure according to the strength parameter. Use cases:
|
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 8 tools
Each tool targets a distinct operation: prompt validation, optimization, explanation, model listing, checkpoint loading, LoRA unloading, text-to-image, and image-to-image. No two tools overlap in purpose, and the descriptions make the boundaries clear.
All tools use a consistent verb_noun pattern in snake_case (validate_prompt, list_models, generate_image). The only slightly longer name is generate_image_from_image, but it follows the same convention clearly.
Eight tools cover the core workflow of prompt preparation, model management, and generation. This is a well-scoped number that avoids unnecessary redundancy.
The server covers the main image generation workflow, including prompt handling and model configuration. Minor gaps exist, such as no explicit checkpoint unload tool, but list_models includes currently loaded state and unload_loras provides a reset path.