This MCP server provides a small Fibonacci computation suite implemented in Python. It exposes three approaches: a recursive Fibonacci function, an iterative Fibonacci function, and a function to generate a sequence of Fibonacci numbers. The server is intended to demonstrate multiple implementation strategies and performance considerations for Fibonacci calculations. To use it, run the server using the provided configuration, then access the available functions via the included module interface or CLI wrappers exposed by fibonacci_server. The recursive approach is simple and readable but becomes slow for larger inputs due to exponential time complexity, while the iterative approach offers much better performance for large numbers. The fibonacci_sequence function generates the first n terms of the sequence for quick visualization or testing.

Typical use cases:

• Compute a single Fibonacci number using the recursive or iterative method (e.g., fib_recursive(n) or fib_iterative(n)).
• Generate a list of the first n Fibonacci numbers using fibonacci_sequence(n).
• Compare performance and behavior between the recursive and iterative implementations for educational or benchmarking purposes.

Prerequisites:

• Python 3.x installed on your system
• Git (optional, for cloning the repository)

Installation steps:

1. Clone the repository (or download the source): git clone https://github.com/your-org/github-mcp-server-practice.git cd github-mcp-server-practice

2. Create and activate a virtual environment (recommended): python -m venv venv

   Windows

   venv\Scripts\activate.bat

   macOS/Linux

   source venv/bin/activate

3. Install dependencies (if a requirements file exists): pip install -r requirements.txt

4. Run the MCP server using the specified mcp_config (the project wiring should point fibonacci_server as a module to execute): python -m fibonacci_server

Note: If a different startup script/module is used in your environment, adjust the command accordingly to reflect the module name or script path. Ensure the module fibonacci_server (or the actual entry point) is importable from your environment.

Tips and notes:

• The recursive implementation can hit Python's maximum recursion depth for larger n; consider using the iterative version for large inputs or tail-recursive optimizations if available.
• If you need to compare performance, you can measure execution times using time or a profiling tool and log results for n values (e.g., n = 10, 20, 30).
• If you modify the code, keep separate functions for fib_recursive, fib_iterative, and fibonacci_sequence to preserve a clean API for benchmarking.
• Ensure that the module entry point (fibonacci_server) exposes the intended functions so the MCP runner can invoke them consistently.