Runtime tool development is emerging as a pivotal advancement in the evolution of AI agents, potentially shaping the future of how these systems interact with the external world. In current AI frameworks, the terms tools, functions, and skills are often used interchangeably, but they essentially serve as the hands and feet of AI agents—enabling them to perform actions beyond language generation. The Model Control Protocol (MCP) aims to standardize this interaction, likening it to a universal digital interface like USB-C, allowing seamless integration across systems. The true power of AI agents lies in their ability to control and interface with external environments, applications, and data sources. As agent capabilities grow, so does the importance of tool search and ranking—especially when an agent has access to a vast array of tools. The challenge isn’t just having tools, but selecting the right one at the right time. While some approaches, such as those championed by Anthropic, focus on building reusable “skills” where the LLM generates the code for a tool, the ultimate goal remains dynamic, on-demand tool creation. The ideal AI agent should be able to autonomously write, test, and refine the integrations it needs during real-time execution. Recent research introduces Test-Time Tool Evolution, a breakthrough that enables AI agents to synthesize, verify, and iteratively improve executable tools during inference. This approach achieved a 62% accuracy rate in generating functional code, demonstrating practical feasibility. Despite these advances, developers still prioritize control and oversight. Most view AI not as a fully autonomous agent but as a sophisticated auto-completer—relying heavily on context awareness and structured planning. While AI agents incorporate these elements, concerns persist around instability and risks tied to self-coding behavior. To mitigate these risks, sandboxed environments and Docker containers remain widely used to safely execute AI-generated code. A key limitation of current AI agent systems is their dependence on static, pre-defined tool libraries. These libraries are often insufficient, fragmented, and inconsistent—especially in complex domains like scientific research. Scientific tools are typically rare, project-specific, and lack standardization, making it nearly impossible to compile a comprehensive, hand-curated toolset in advance. The new paradigm shifts from rigid, static libraries to dynamic, on-demand tool generation. In this model, AI agents begin with an empty tool library, then create custom Python functions tailored to the task at hand. These functions are validated in isolated environments, then broken down into reusable atomic components. This approach not only enhances adaptability but also enables agents to evolve their capabilities in real time, unlocking greater potential across diverse and unpredictable problem spaces.