AI agents are advancing in their ability to perform real-world tasks by using tools and systems, necessitating a blend of accurate responses, complex environment navigation, and human-like adaptability. This evolution highlights the importance of tool integration to enhance an agent's capabilities, such as querying databases, triggering APIs, and interacting with user interfaces, which addresses inherent limitations like poor arithmetic and outdated knowledge. Labelbox's integration of Multimodal Chat (MMC) editor with an MCP server streamlines the evaluation of these tool-based behaviors by allowing AI teams to inspect, label, and edit agent-tool interactions. This integration enables more precise evaluations and human feedback for tool-augmented agents, supporting the development of robust systems that can better capture human intent and preferences. By setting up an MCP server using tools like FastMCP, and configuring the Labelbox project for tool use, developers can move from static data labeling to interactive evaluation, enhancing the reliability and effectiveness of AI agents in applications such as customer support and enterprise workflows.