Why is there MCP Tool Overload and how to solve it for your AI Agents

AI agents using the Model Context Protocol (MCP) can face significant challenges when connected to an excessive number of tools, leading to prompt bloat, slower response times, increased costs, and a higher risk of errors or unsafe tool usage. This tool overload occurs because large language models (LLMs) struggle with too many unnecessary options, which can confuse reasoning and lead to poor tool selection or even hallucinations of nonexistent tools. The issue is exacerbated by the modular nature of MCP, where tools from various systems can be easily aggregated, often resulting in an unwieldy and inefficient setup. To address this, the concept of "Tool Groups" is introduced, allowing agents to access only the tools necessary for specific workflows, thereby reducing context window consumption and improving overall agent performance. This approach not only enhances agent accuracy and reduces costs but also mitigates security risks and helps manage platform-imposed tool limits effectively, ensuring more efficient and reliable AI operations.