How would you use an LLM in a Python program? What is the recommended way to call the LLM API? Also, what is MCP?

To use an LLM in a Python program you typically follow these steps: 1. Install the SDK (e.g., pip install openai for OpenAI models). 2. Store your API key securely (environment variable or secret manager). 3. Import the library and set the key: openai.api_key = os.getenv('OPENAI_API_KEY'). 4. Call the desired endpoint, such as openai.ChatCompletion.create(model='gpt‑4', messages=[{'role':'user','content':'Explain recursion.'}]). 5. Handle the response, stream if needed, and add error handling for rate limits or timeouts. 6. Optionally wrap the call in a helper function to abstract the API details. 7. For production, consider caching responses, logging usage, and monitoring costs. 8. If you need to fine‑tune or embed, use the corresponding endpoints. 9. Always respect the provider’s usage policies. 10. For a quick prototype, you can also use the langchain or transformers libraries to load a local model. 11. Regarding MCP, it can mean Microcontroller Peripheral or Microsoft Certified Professional. In the context of LLM usage, the term is unlikely to be relevant unless you’re discussing hardware acceleration (e.g., using an MCP‑based FPGA for inference).