MCP Calculator Server: Universal Adapter for AI Applications

Overview: What is MCP Calculator Server?

The MCP Calculator Server is an implementation of Model Context Protocol (MCP) tailored for basic arithmetic operations in Smithery and other AI environments. By adhering to the MCP standards, this server allows seamless integration with various AI applications such as Claude Desktop, Continue, and Cursor, providing a robust platform for executing simple yet essential mathematical functions.

🔧 Core Features & MCP Capabilities

This server supports core MCP functionalities, making it versatile for different deployment scenarios:

• Basic Arithmetic Operations: Supports fundamental arithmetic operations like addition, subtraction, multiplication, and division.
• REST API Endpoints: Offers RESTful endpoints compliant with the MCP protocol, enabling seamless communication between clients and servers.
• JSON Schema Validation: Ensures data integrity by validating incoming requests against defined schemas.
• Error Handling Mechanism: Provides comprehensive error handling for common issues such as invalid operations, division by zero, insufficient operands, and type mismatches.

Communication Modes

The server supports multiple communication modes to cater to various integration needs:

• HTTP Mode (Recommended for Production & Containers): Ideal for production deployments and container environments where reliable HTTP endpoints are required.
• Smithery Mode: Specifically designed for local tool integration with Smithery, using standard input/output streams.
• Dual Mode (Development Only): Allows running both HTTP and stdio modes simultaneously for development purposes.

⚙️ MCP Architecture & Protocol Implementation

Server Structure

The server architecture is modular, consisting of the following key components:

1. HTTP API Endpoints: Exposes arithmetic operations through RESTful endpoints.
2. MCP Compliant APIs: Implements both JSON-RPC and WebSocket interfaces for MCP client integration.
3. JSON Schema Validation Modules: Ensures data validation according to predefined schemas.
4. Error Handling Logic: Manages errors and provides appropriate responses.

Mapped Data Flow

The following Mermaid diagram illustrates the flow of data between an AI application, the MCP server, and underlying tools:

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

MCP Client Compatibility Matrix

To ensure seamless integration, the server supports specific MCP clients with varying levels of support:

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

This matrix indicates that both Claude Desktop and Continue fully support resource, tool, and prompt management via this server.

🚀 Getting Started with Installation

Environment Setup

1. Create a Virtual Environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   

2. Install Dependencies:

   pip install -r requirements.txt
   

Running the Server

HTTP Mode (Recommended for Production & Containers)

To run the server in pure HTTP mode, use:

# Set environment variable to ensure only HTTP mode runs
export MCP_HTTP_MODE=1  # On Windows: set MCP_HTTP_MODE=1
uvicorn server:app --host 0.0.0.0 --port 8000

Or use the provided script for smoother execution:

# On Unix/Linux/Mac
./start-container.sh

# On Windows
start-container.bat

Smithery Mode (Local Tool Integration)

For local tool integration, run the server in stdio mode:

# Set environment variable to ensure only stdio mode runs
export MCP_STDIO_MODE=1  # On Windows: set MCP_STDIO_MODE=1
python server.py

Use the provided convenience scripts for a hassle-free experience:

# On Unix/Linux/Mac
./start-smithery.sh

# On Windows
start-smithery.bat

Dual Mode (Development Only)

For simultaneous HTTP and stdio mode testing, simply run:

python server.py

This is recommended only for development purposes due to the potential issues with stdin handling.

💡 Key Use Cases in AI Workflows

Workflow 1: Real-time Data Processing

In real-world applications, this server can be part of a larger system where it receives data from multiple sources and processes them using arithmetic operations. For example:

• Data Ingestion: Collects sensor readings at regular intervals.
• Arithmetic Operations: Utilizes the add function to sum up various data points.
• Result Processing: Sends processed results back for further analysis or storage.

Workflow 2: Custom Scripts and Prompts

AI applications often require custom scripting capabilities. This server enables users to define and execute custom arithmetic operations:

• Prompt Definition: Define a script that performs complex calculations based on user inputs.
• Operation Execution: Use the execute function to run predefined scripts with various parameters.
• Result Handling: Handle results from complex operations seamlessly.

🔌 Integration with MCP Clients

The server is compatible with multiple MCP clients, ensuring smooth interaction and versatile use cases. The following configuration ensures successful integration:

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

Ensure you customize the server name, command, and environment variables according to your specific setup.

📊 Performance & Compatibility Matrix

This matrix evaluates the performance and compatibility of the MCP Calculator Server with various environments:

Environment	API Endpoints Accessibility	Error Handling Effectiveness	Integration Success Rate
Production	High	Good	Excellent
Containers	Perfect	Reliable	Seamless
Local Development	Partial	Basic	Functional

This matrix highlights the server’s performance across different scenarios, ensuring reliable operation in all environments.

🛠️ Advanced Configuration & Security

Environment Variables

Set environment variables to tailor the server's behavior:

• MCP_HTTP_MODE: Ensure HTTP mode for production.
• MCP_STDIO_MODE: Enable stdio mode for local Smithery integration.
• LOGGING_CONFIG: Configure logging using desired method.

Security Considerations

• API Key: Secure API keys when running in a production environment.
• Authentication: Utilize authentication mechanisms to protect sensitive operations.
• Firewall Rules: Implement appropriate firewall rules to restrict access based on IP addresses.

❓ Frequently Asked Questions (FAQ)

1. Is the server compatible with all MCP clients?

   • While most major MCP clients like Claude Desktop and Continue are fully supported, compatibility may vary for some custom clients.

2. How can I troubleshoot HTTP mode issues?

   • Check server logs for detailed error messages and review the configuration settings to ensure they align with your environment.

3. Can I use this server in a local development setup using Docker?

   • Yes, you can containerize the server using Docker. Ensure you map host ports correctly when running Docker containers.

4. What is the fallback behavior for unsupported operations?

   • The server returns an appropriate error message and HTTP response code to indicate that the operation is not supported.

5. How do I handle large-scale data processing with this server?

   • Break down large datasets into manageable chunks and process them sequentially or in parallel using batch processing techniques.

Conclusion

The MCP Calculator Server stands out as a powerful tool for developers building AI applications, offering robust arithmetic capabilities through Model Context Protocol (MCP). Its modular architecture and flexible deployment options make it suitable for both development and production environments. Whether you need to perform simple calculations or implement complex scripts, this server provides the necessary infrastructure to ensure seamless interaction with various MCP clients.

By leveraging this server’s features, developers can enhance their AI applications, improving functionality and integration across different platforms. For more information on integrating this server into your projects, consult the official documentation or reach out for support through community forums and developer resources.

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This comprehensive guide should help you understand and utilize the MCP Calculator Server effectively in your AI application development journey.