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# MooseCP Image Server
A Model Context Protocol (MCP) server designed to provide LLMs with efficient, token-optimized visual access to image directories. Instead of dumping full-resolution images (which waste tokens and cause context overflow), MooseCP provides a hierarchical workflow: **List $\rightarrow$ Scan $\rightarrow$ Preview $\rightarrow$ Inspect**.
A Model Context Protocol (MCP) server designed to provide LLMs with efficient, token-optimized visual access to image directories and AI generation capabilities. Instead of dumping full-resolution images (which waste tokens and cause context overflow), MooseCP provides a hierarchical workflow: **List $\rightarrow$ Scan $\rightarrow$ Preview $\rightarrow$ Inspect**.
## ⚠️ AI SLOP DISCLAIMER
This entire project was vibe-coded by an AI. It is 100% slop code. Use it at your own risk.
@@ -32,6 +32,17 @@ Extracts AI generation parameters from PNG files.
Returns the full-resolution image.
* **Best for**: Final confirmation or deep visual analysis where every pixel counts.
### 🎨 `generate_image`
Triggers an image generation on a local Stable Diffusion WebUI Forge instance.
* **Workflow**: Always call `get_model_info` first to determine the correct prompting style (e.g., tag-based vs. natural language).
* **Features**: Supports model-specific presets, resolution presets, and standard parameter overrides.
* **Output**: Returns a Base64 image for AI analysis and a proxy URL for direct embedding in the chat.
### ℹ️ `get_model_info`
Provides the "manual" for available generation models.
* **Best for**: Learning the prompting style, recommended settings, and available resolution presets for a specific model.
* **Workflow**: Call without arguments to see the catalog; call with `model_name` for the detailed guide.
### 🌐 `browse_wikipedia`
Allows the model to browse Wikipedia using its API.
* **Best for**: Quickly retrieving summaries, structural maps (ToC), or specific section content from Wikipedia without dumping the entire page.
@@ -47,9 +58,10 @@ This server requires Python 3.10+ and the following packages:
* `uvicorn`: ASGI server for the SSE transport.
* `starlette`: Lightweight ASGI framework.
* `Pillow`: Image processing and thumbnail generation.
* `requests`: For communicating with the Stable Diffusion API.
```bash
pip install uvicorn starlette Pillow
pip install uvicorn starlette Pillow requests
```
### Setup