Model Context Protocol (MCP) Server

This is an MCP Server designed to enable AI applications such as Claude Desktop, Continue, and Cursor to interact seamlessly with various tools and data sources through a standardized protocol. This comprehensive documentation will guide you through setting up and using this server.

Overview: What is an MCP Server?

An MCP Server acts as a bridge between AI applications and external tools or data sources. It adheres to the Model Context Protocol (MCP), which ensures that different AI applications can access and utilize the same set of tools in a consistent manner, similar to how USB-C supports multiple device types.

🔧 Core Features & MCP Capabilities

The core features of this MCP Server include:

1. Standardized Interface: The server provides a standardized interface based on the MCP protocol, allowing AI applications like Claude Desktop, Continue, and Cursor to connect with various tools.
2. Tool Discovery: It automatically discovers available tools from external sources and makes them discoverable by connected AI applications.
3. Secure Communication: Integrates an Anthropic API key for secure communication between the client and server.
4. Resource Management: Efficiently manages resources, ensuring smooth performance during tool executions.

⚙️ MCP Architecture & Protocol Implementation

The architecture of this MCP Server is designed to be flexible and adaptable to different AI applications. It includes:

1. MCP Client Compatibility Matrix: Ensures compatibility with various AI applications.
2. Tool Execution Flow: A detailed flow diagram illustrating the interaction between the client, server, and external tools.

MCP Protocol Flow Diagram

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[External Tool/DataSource]
    style A fill:#e1f5fe
    style B fill:#ddeaeb
    style C fill:#ffdb8c
    style D fill:#bde3a1

MCP Client Compatibility Matrix

MCP Client	Resources	Tools	Prompts
Claude Desktop	✅	✅	✅
Continue	✅	✅	✅
Cursor	❌	✅	❌

🚀 Getting Started with Installation

Prerequisites:

1. Python 3.11 or higher
2. An Anthropic API key

Installation Steps:

1. Clone this repository:

   git clone <repository-url>
   

2. Ensure you have the required dependencies installed:

   pip install anthropic mcp python-dotenv
   

3. Set up your Anthropic API key in a .env file:

   ANTHROPIC_API_KEY=your_api_key_here
   

💡 Key Use Cases in AI Workflows

Case 1: Financial Analysis for Investment Decisions

Scenario: An investment firm needs to analyze financial data from multiple sources, such as stock prices and economic indicators.

Implementation:

• The MCP Server connects to a finance API tool.
• Claude uses the server to gather real-time market data.
• Tools are executed seamlessly within the AI application.

Case 2: Medical Diagnosis Assistance

Scenario: A healthcare provider wants to assist doctors with preliminary diagnosis by integrating medical research tools and patient records.

Implementation:

• The MCP Server integrates with medical databases and research tools.
• Doctors can query the system for patient-specific information.
• Results are delivered in a format compatible with existing systems.

🔌 Integration with MCP Clients

The server supports interaction with multiple AI clients, including:

1. Claude Desktop: Provides full support for tool integration and resource management.
2. Continue: Fully compatible without additional configuration.
3. Cursor: Supports tools but lacks prompt integration capabilities.

Code Sample: MCP Configuration

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

📊 Performance & Compatibility Matrix

Feature	Status
Tool Discovery	✅
Secure Communication	✅
Resource Management	✅

This matrix indicates that the server fully supports all critical features required for seamless integration.

🛠️ Advanced Configuration & Security

Security Measures:

1. API Key Protection: The Anthropic API key is stored securely in the .env file.
2. Data Encryption: Any sensitive data transmitted between the client and server is encrypted.

Configuration Options:

• Environment Variables: Customize settings using environment variables defined in the .env file.
• Custom Commands: Modify execution commands to suit specific needs.

❓ Frequently Asked Questions (FAQ)

1. Why is there no prompt support for Cursor?

   • Cursor does not currently have built-in support for prompts, hence it only supports tool executions.

2. How can I troubleshoot connection issues?

   • Verify the server path and ensure that the server is running correctly.

3. What are the performance implications of using multiple tools simultaneously?

   • The server optimizes resource management to handle multiple tool executions efficiently.

4. Can I customize environment variables for different AI clients?

   • Yes, you can set custom environment variables per client in the .env file.

5. How does the server ensure data privacy during interactions with external tools?

   • Data encryption and secure communication protocols are used to protect sensitive information.

👨‍💻 Development & Contribution Guidelines

Developers contributing to this project should follow these guidelines:

1. Forking: Fork the repository before making any changes.
2. Branching: Create a new branch for your specific features or bug fixes.
3. Testing: Ensure all tests pass and include new test cases if necessary.
4. Documentation: Update documentation to reflect changes in the codebase.

🌐 MCP Ecosystem & Resources

Related Documentation:

• Model Context Protocol Documentation
• Anthropic API Documentation

This comprehensive documentation provides a clear understanding of how to set up and utilize the Model Context Protocol (MCP) Server, ensuring seamless integration with various AI applications.