MooseCP/README.md

MooseCP Image Server

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.

Tools Overview

📁 list_directory

Provides a simplified file-browser view of a directory.

• Best for: Getting a sense of the files present and their basic metadata (size, date).
• Features: Pagination and sorting by name, size, or modification date.

🖼️ contact_sheet

Generates a high-density grid of thumbnails.

• Best for: Quickly scanning hundreds of images to find a specific one or get a general "vibe" of a folder.
• Note: This tool is paginated. You must iterate through pages to see all images in a folder.
• Workflow: Use the indices shown on the contact sheet to call preview_image.

🔍 preview_image

Provides medium-detail previews optimized for the model's token budget.

• Best for: Comparing a few candidates, inspecting specific details, or selecting a "favorite" image.
• Efficiency: Automatically calculates dimensions to fit the model's patch size (e.g., 48px patches for Gemma 4), ensuring maximum detail without wasting tokens on padding.
• Workflow: Pass indices from the contact_sheet or a direct file path.

📖 read_png_metadata

Extracts AI generation parameters from PNG files.

• Best for: Retrieving prompts, seeds, and model hashes from AI-generated images.

📸 read_image

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.
• Workflow: Use mode='summary' (default) to get an overview and a Table of Contents. Use mode='section' with a linear index from the ToC to dive into specific details.
• Features: Returns raw Wikitext to save tokens, handles redirects, and automatically falls back to a search result list if a page is not found.

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Installation & Requirements

Dependencies

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.

pip install uvicorn starlette Pillow requests

Setup

1. Clone this repository to your server.
2. (Optional) Edit config.py to adjust the server port, log level, or token budgets if you are using a model other than Gemma 4.
3. Run the server:

   python main.py
   

Configuration (config.py)

You can tune the server's behavior in config.py:

Server & Logging

• HOST / PORT: The network address and port the server binds to.
• LOG_LEVEL: Logging verbosity (DEBUG, INFO, WARNING, ERROR).
• LOG_FILE: Path to the server log file.
• USER_AGENT: The User-Agent string used for API requests (e.g., Wikipedia). Use a browser-like string to avoid 403 Forbidden errors.

Model & Token Tuning

• PATCH_SIZE: Set this to (clip.vision.patch_size * n_merge) for your specific model to ensure token-perfect resizing.
• PREVIEW_TOKEN_BUDGET: Controls how many tokens the preview_image tool aims for (default: 70).
• CONTACT_SHEET_COLS / ROWS: Adjust the grid size to fit within your model's maximum context window.
• CONTACT_SHEET_THUMB_SIZE: The pixel size of thumbnails in the contact sheet.

Image & Font Settings

• IMAGE_QUALITY: Adjust JPEG compression (1-100).
• SYSTEM_FONT_NAMES: A list of font names Pillow should attempt to find in the system path.
• FALLBACK_FONT_PATHS: Absolute paths to .ttf files used if no system fonts are found.