Worked on the pydantic-ai repository to enhance model interoperability and tool integration by improving how tool outputs are parsed and validated. Addressed scenarios where arguments are returned as JSON strings nested under a 'response' key, implementing lenient validation to support both direct JSON strings and structured dictionaries. Expanded test coverage to verify handling of lists of Pydantic models, ensuring compatibility with diverse model providers. In the MCP tool, refactored result extraction to prioritize structured content and improved error handling for failed tool calls. Utilized Python, Pydantic, and robust testing practices to deliver reliable backend and API integration solutions.