In our previous post, we constructed a minimal MCP Client hub, a lightweight FastAPI service designed to discover tools from specialized servers and route execution requests. While this hub offers a clean interface, it is only part of the solution. Now, we need to integrate the intelligence: the LLM Host that determines which tool to use and when. The challenge goes beyond merely connecting to an LLM; it involves creating an abstraction layer that allows us to switch models seamlessly without impacting our tool ecosystem. Today, we might opt for models like o3, GPT-4.1, Llama-4-Maverick, DeepSeek R1/V3, or Claude Sonnet 3.7. However, the landscape of language models is rapidly evolving, with new models being released monthly. We might also consider local LLMs, such as those provided by Ollama or LMStudio. Our architecture must accommodate this flexibility while ensuring consistent interactions with our tools. To achieve this, we will implement two crucial components. First, a provider system that abstracts the specific details of each model's API. Whether we are using OpenAI’s API, Groq’s OpenAI-compatible endpoint, or Anthropic’s service, the rest of our code should remain agnostic to these differences. This ensures that changing models is a straightforward process that does not necessitate extensive code modifications. Second, we will develop a robust router system that intelligently selects the most appropriate LLM for each request based on various criteria, such as model capabilities, performance, and cost. The router will act as a central decision-making point, enhancing the efficiency and adaptability of our MCP architecture. By combining these two components, we can create a flexible and scalable system. The provider system will handle the intricacies of different LLM APIs, while the router will ensure that the best model is chosen for each task. This approach not only simplifies future updates and additions but also optimizes resource utilization and enhances overall performance. To illustrate, let’s break down how these components work together. When a user sends a request to the MCP Client hub, the hub forwards the request to the LLM Host. The LLM Host uses the provider system to translate the request into a format compatible with the chosen language model. Simultaneously, the router evaluates available models against the task requirements and selects the one that best meets the criteria. Once the appropriate model is chosen, the provider system facilitates communication with that model, ensuring a smooth and consistent interaction. This design allows us to easily add or remove models without disrupting the tool ecosystem. It also makes it simpler to switch between cloud and local models based on real-time needs, such as latency, data privacy, or cost constraints. For example, if a cloud-based model experiences high latency or increased costs, the router can dynamically switch to a local model to maintain optimal performance and cost efficiency. Moreover, this architecture supports advanced features like model evaluation and monitoring. The router can track the performance of each model over time and make data-driven decisions to improve the overall system. Performance metrics, such as accuracy, response time, and cost, can be used to fine-tune the model selection process, ensuring that users always have the best experience. In conclusion, integrating an LLM Host with a provider system and a smart router is essential for creating a flexible, scalable, and efficient MCP architecture. These components not only simplify the management of multiple language models but also optimize resource usage, ensuring that our system remains responsive and cost-effective, even as the LLM landscape continues to evolve.