Worked on the mastra-ai/mastra repository to enhance the reliability of tool execution pipelines by addressing input validation challenges. Focused on improving how null values from large language model responses are detected, replacing error-message matching with a value-based approach in TypeScript. This adjustment reduced the risk of misclassifying null results, leading to more robust validation and higher system uptime. Collaborated with other contributors to merge these changes into the core codebase, emphasizing thorough testing and validation practices. The work demonstrated attention to detail in handling diverse LLM outputs and contributed to more dependable tool input validation across the project.