Creating an MCP-Inspired Routed AI Agent System with Adaptive Tool Management

In this guide, we will construct a routed AI agent system modeled after the MCP framework. This system will seamlessly integrate tool discovery, intelligent routing, and structured execution into a unified workflow. We begin by establishing a modular tool server that provides functionalities such as web searching, local data retrieval, and executing Python code, all organized through defined schemas. Next, we create a hybrid routing mechanism that employs both heuristic methods and large language model (LLM) reasoning to determine which tools to make available for specific tasks, ensuring a focused and efficient tool exposure. As we advance, we develop an agent capable of planning tool usage, executing tasks securely, and synthesizing responses by incorporating context derived from tool outputs. Ultimately, we illustrate the application of this system across various real-world scenarios, demonstrating how principles such as context integration, routing strategies, and controlled tool access work together to form a scalable and interpretable agent system.