Tiny Cryptography MCP Server: Secure Interoperability for AI Applications

Overview: What is Tiny Cryptography MCP Server?

Tiny Cryptography MCP Server is an open-source implementation of the Model Context Protocol (MCP) built with Express.js and powered by the Stanford Javascript Crypto Library (SJCL). This server offers a robust set of cryptographic tools, including key pair generation, secure shared secret derivation, encryption using AES-CCM, and decryption functionality. It also supports Server-Sent Events (SSE) for real-time communication, making it a powerful tool for enhancing security and interoperability in AI applications.

🔧 Core Features & MCP Capabilities

Tiny Cryptography MCP Server is designed to provide essential cryptographic capabilities underpinned by the Model Context Protocol. It includes core features such as generating SJCL P-256 key pairs, deriving shared secrets using private and public keys, encrypting messages with AES-CCM encryption, and decrypting these encrypted messages. These tools enable secure and reliable communication between different AI applications, ensuring that sensitive information remains protected during transit.

Key Features:

1. Key Pair Generation: Generate SJCL P-256 key pairs for secure data exchange.
2. Shared Secret Derivation: Derive shared secrets from private and public keys to ensure secure communication.
3. Message Encryption/Decryption: Encrypt messages using AES-CCM encryption with a derived shared secret, ensuring that only authorized parties can access the content.

AI Application Integration:

This server supports integration with various AI applications such as Claude Desktop, Continue, Cursor, and others through the Model Context Protocol. It provides a standardized interface for these applications to communicate securely and efficiently.

⚙️ MCP Architecture & Protocol Implementation

The architecture of Tiny Cryptography MCP Server is designed to ensure secure and interoperable communication between different components in AI applications. The server follows the Model Context Protocol (MCP) standard, which defines how AI models and tools can interact with each other through a common framework.

Protocol Flow Diagram:

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 Diagram:

graph TD;
  A{AI Application} --> B[Model Context Protocol (MCP)] --> C[MCP Server] --> D[Data Source/Tool];
  E[Key Pair Generation] --> F[Shared Secret Derivation] -[:Derive Shared Secret from Keys:]--> G[Message Encryption by MCP Server];
  H[Secure Communication] --> I{Client-Side Decryption};

🚀 Getting Started with Installation

To set up and run the Tiny Cryptography MCP Server, follow these steps:

1. Clone the Repository:

   git clone https://github.com/anton10xr/gibber-mcp
   cd mcp-server
   

2. Install Dependencies:

   npm install
   

3. Start the Development Server:

   npm run dev
   

4. Start the Production Server:

   npm start
   

💡 Key Use Cases in AI Workflows

1. Secure Data Exchange between AI Models: Ensure that sensitive information is exchanged securely between different AI models using encryption and secure key management.
2. Real-Time Communication for Monitoring Systems: Leverage SSE to enable real-time updates and monitoring of AI applications, ensuring data integrity and security.

Use Case 1: Secure Data Integration

Imagine an AI application tasked with integrating data from multiple sources. The MCP Server ensures that all interactions between these data sources are secure by generating key pairs and deriving shared secrets for encryption. This process guarantees that only authorized parties can access the integrated data, maintaining privacy and confidentiality.

Implementation Steps:

• Generate Key Pairs: Use the server to generate P-256 keys.
• Derive Shared Secret: The shared secret derived from these keys is used for subsequent encrypted communications between the AI models.
• Encrypt Data: Encrypt the merged data using AES-CCM before transmission.

Use Case 2: Real-Time Monitoring and Alerting

In a scenario where real-time monitoring of an AI application's performance is critical, the MCP Server can facilitate seamless communication. The server supports SSE to notify other components in the system about changes or anomalies in real time. This ensures that all parties involved are up-to-date with any ongoing issues.

Implementation Steps:

• Set Up SSE: Configure the MCP Server to emit events via SSE.
• Receive Events: Implement listeners on the client-side to receive and process these events.
• Alerting Mechanism: Use the received data to trigger alerts or take corrective actions as needed.

🔌 Integration with MCP Clients

Tiny Cryptography MCP Server is designed to be compatible with various AI applications through the Model Context Protocol. The following table outlines the current status of support for MCP clients:

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Full Support
Cursor	❌	✅	❌	Tools Only

📊 Performance & Compatibility Matrix

To ensure compatibility and optimal performance, the server has been tested against various AI applications:

• Performance Metrics: The server supports high volumes of encrypted data exchange with minimal latency.
• Compatibility Testing: Extensive testing has ensured seamless integration across different environments.

Example MCP Configuration Code Sample:

{
  "mcpServers": {
    "[server-name]": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-[name]"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

🛠️ Advanced Configuration & Security

To ensure robust security and performance, the following settings can be configured:

1. API Key Authentication: Secure access to the MCP Server by enforcing API key validation.
2. Environment Variables: Customize environment variables to tailor server behavior according to specific requirements.
3. Security Protocols: Implement additional security measures such as HTTPS and secure data storage.

Security Best Practices:

• Use API Keys for Access Control: Ensure that only authorized parties can access the MCP Server by validating API keys.
• Enable SSL/TLS: Protect all data transferred over the network using SSL/TLS encryption.
• Regular Audits: Conduct regular security audits to identify and mitigate potential vulnerabilities.

❓ Frequently Asked Questions (FAQ)

Q1: How does Tiny Cryptography MCP Server ensure secure communication between AI applications?

Answer: The server uses SJCL P-256 key pairs for generating keys, derives shared secrets for encryption using AES-CCM, and leverages secure protocols to protect data during transmission.

Q2: What are the supported MCP clients for this server?

Answer: The client compatibility matrix includes support for Claude Desktop, Continue, and Cursor. However, full support is currently available only for tools in these clients.

Q3: How can I set up an environment variable to customize the behavior of the MCP Server?

Answer: Customize environment variables when configuring the MCP server by adding them under the env field in the configuration file.

Q4: What are the steps for setting up real-time communication with the MCP Server using SSE?

Answer: Set up SSE on the client-side to receive events and handle dynamic updates. Use the provided example configuration as a guide.

Q5: Are there any known issues or limitations with running this server in production environments?

Answer: While the server is robust, it may require additional testing and tuning for specific use cases. Ensure regular monitoring and update procedures are in place to address any potential issues.

👨‍💻 Development & Contribution Guidelines

Interested developers can contribute to the development of Tiny Cryptography MCP Server by following these guidelines:

1. Clone the Repository:

   git clone https://github.com/anton10xr/gibber-mcp.git
   cd gibber-mcp/mcp-server
   

2. Set Up Development Environment:

   • Install dependencies using npm.
   • Run tests to verify functionality.

3. Contribute Code:

   • Fork the repository and make changes.
   • Ensure your code passes all existing tests.
   • Commit and push your changes.

4. Request a Pull Request: Submit a pull request for review and merging into the main branch.

🌐 MCP Ecosystem & Resources

For more information on Model Context Protocol (MCP), visit the official Model Context Protocol documentation. The community also maintains a growing collection of resources, including tutorials, case studies, and best practices for integrating with MCP servers.

By leveraging Tiny Cryptography MCP Server, developers can create secure and interoperable AI applications that meet modern security requirements.