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. 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 server is designed to facilitate the interaction between AI models and image datasets. While it provides tools for "previewing" and "inspecting" images, remember that AI models can still hallucinate visual details, especially when working with low-resolution previews. Always verify critical visual information with the read_image tool (full resolution) before drawing final conclusions.

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.

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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.

pip install uvicorn starlette Pillow

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:

• 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.
• IMAGE_QUALITY: Adjust JPEG compression (1-100).