PAD MCP Server: Standardized Interface for Paper Analytical Devices (PAD)

Overview: What is PAD MCP Server?

The PAD MCP Server implements a comprehensive interface designed to interact with the Paper Analytical Devices (PAD) system at Notre Dame, specifically at pad.crc.nd.edu. This server plays a crucial role in enabling machine learning (LLM)-based tools and agents to access and process PAD data through the Model Context Protocol (MCP). It serves as a standardized bridge between advanced AI applications like Claude Desktop, Continue, Cursor, and other MCP clients, allowing seamless integration with data management, neural network analysis, and image processing workflows.

🔧 Core Features & MCP Capabilities

The PAD MCP Server offers a wide array of features that cater to the diverse needs of AI application developers and researchers. These capabilities are essential for maintaining secure and efficient interactions between different tools and systems through the MCP protocol:

Card Management:

• Metadata Retrieval: The server can retrieve and list all available PAD test cards, providing comprehensive metadata such as camera type, quantities, and IDs.
• Card Details Access: Detailed card information is accessible, including configurations like image processing details and storage paths.

Neural Network Integration:

• Network Inventory: List of available neural networks at the client's disposal.
• Configuration Management: Ability to access detailed network configurations for seamless integration with analysis systems.

Project Organization:

• Managed Projects: Server supports project management functionalities such as creating, listing, and tracking projects.
• Card Groups & Issues Sample Handling: Card groups are handled along with sample tracking for better organization and traceability.

Image Processing:

• Advanced Resizing: Employing high-quality Lanczos resampling techniques to resize images while maintaining their quality.
• Optimization & Automation: Automatic processing to ensure image dimensions do not exceed 300px in width, enhancing efficiency.
• Coordinate Scaling for Card Geometry: Efficient handling of card geometry and coordinate scaling with detailed metadata integration.

⚙️ MCP Architecture & Protocol Implementation

Protocol Flow Diagram

The MCP protocol flow from an AI application perspective is essential to understand its operation:

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

Data Architecture

The data architecture within the PAD MCP Server ensures secure and efficient interaction between systems:

graph TD
    subgraph Data Flow
        A[AI Application]
        B[MCP Client]
        C[Dataloader Module]
        D[Model Context Protocol (MCP)]
        E[MCP Server]
        F[Data Source / Tool]
        A -->|API Calls| B
        B --> C
        C -->|JSON Queries| D
        D --> E
        E -->|Protocol Packets| F
    end

🚀 Getting Started with Installation

Setting Up for Claude Desktop Users

To begin utilizing the PAD MCP Server with Claude Desktop, follow these steps:

1. Ensure Python 3.12 or higher is installed.
2. Clone the repository containing this server.
3. Configure Claude Desktop with the provided settings.

Configuration Details:

{
  "mcpServers": {
    "pad": {
      "command": "<path-to-uv>",
      "args": [
        "--directory",
        "<path-to-repository>",
        "run",
        "main.py"
      ],
      "env": {
        "FILESYSTEM_STORAGE": "<path-to-storage-directory>"
      }
    }
  }
}

Replacement variables:

• <path-to-uv>: Path to uv installation (example: ~/.local/bin/uv)
• <path-to-repository>: Absolute path where the repository is cloned
• <path-to-storage-directory>: Local directory for storing PAD files (~/Documents/pad_storage by default)

Running the Server

To manually run the server:

uv run python pad.py

This setup ensures that all necessary dependencies are managed automatically through uv, allowing a straightforward configuration process.

💡 Key Use Cases in AI Workflows

Case Study 1: Card Image Analysis for Drug Testing

Workflow Implementation:

• Card Retrieval: Use the get_v2_cards() function to obtain a list of PAD cards.
• Image Processing: Employ get_card_image_by_id(cards_id) to retrieve and process images using the server’s high-quality resizing algorithms.
• Data Storage: Store processed images in the local directory configured in UV.

Case Study 2: Project Management for Research

Workflow Implementation:

• Project Listing: Use get_v2_projects() function to access all PAD projects.
• Card Group Handling: Manage card groups and tracking issues with dedicated functions like get_v2_card_by_id(cards_id).

🔌 Integration with MCP Clients

The PAD MCP Server supports integration with several MCP clients:

MCP Client Compatibility Matrix

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	❌	Tools Only
Cursor	❌	✅	❌	No Data Access

This matrix highlights the extensive support for various MCP clients, ensuring broad compatibility and enhanced workflow capabilities.

📊 Performance & Compatibility Matrix

The PAD MCP Server is designed to perform optimally with high volumes of data exchange and concurrent user access. It excels in maintaining seamless integrations across diverse AI applications:

Tool Integration Levels

Tool	Resource Access	Processing Capabilities
Claude Desktop	Full	Full
Continue API	Partial	Limited

These integration levels ensure that different tools and applications can harness the full potential of PAD data.

🛠️ Advanced Configuration & Security

MCP Configuration Sample

Below is an example configuration for use with the server:

{
  "mcpServers": {
    "pad": {
      "command": "uv",
      "args": [
        "--directory",
        "/Users/csweet1/Documents/projects/earth616/mcp-server-pad",
        "run",
        "main_swagger.py"
      ],
      "env": {
        "SERVER_BASE_URL": "https://pad.crc.nd.edu",
        "OPENAPI_SPEC_URL": "https://pad.crc.nd.edu/api-ld/v3/openapi.json",
        "FILESYSTEM_STORAGE": "/Users/csweet1/Documents/projects/earth616/pad_storage"
      }
    }
  }
}

Security Considerations

All interactions are managed securely through the PAD API, ensuring a robust and safe environment for data handling.

❓ Frequently Asked Questions (FAQ)

1. How does the server ensure secure communication?

   • The PAD MCP Server utilizes secure endpoints and HTTPS communications to protect data during transmission.

2. What is the default storage path for the server?

   • By default, the storage directory is ~/Documents/pad_storage. However, this can be overridden in the configuration.

3. Can Claude Desktop use any networked server?

   • Yes, as long as it meets the MCP protocol and provides relevant APIs, multiple servers can be configured for different purposes.

4. How does image processing work with the PAD cards?

   • The server uses Lanczos resampling and metadata handling to efficiently resize card images while ensuring quality.

5. Is there support for custom neural networks?

   • Custom neural networks can be integrated through the provided APIs, allowing for flexibility in data analysis.

👨‍💻 Development & Contribution Guidelines

Contributions are welcome! Developers interested in contributing should:

1. Follow the existing codebase and best practices.
2. Use the latest version of Python (3.12 or higher).
3. Join the discussion forums to get insights into ongoing projects and enhancements.
4. Submit pull requests with detailed explanations for any changes.

🌐 MCP Ecosystem & Resources

For more information about MCP, visit modelcontextprotocol.io. Explore the resources available there to understand better how this protocol can benefit your AI application integration projects.

This documentation positions the PAD MCP Server as a crucial tool in enhancing AI workflows and provides a comprehensive understanding of its capabilities and integrations.