joao-aires/ai-python-development
Python provider/FastMCP integration, DeepEval testing and Python OpenTelemetry observability.
Implement accepted Python model/provider adapters and standalone FastMCP interfaces. Use for Gemini integration, version-matched Python MCP APIs and interaction integration; preserve established providers and frameworks.
Implement Python AI evaluation suites with DeepEval for model, retrieval, agent trajectory, conversation and end-to-end quality. Use when DeepEval is selected or being evaluated; preserve existing evaluation tools and do not introduce model calls, hosted reporting or synthetic generation without scope.
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Set up and verify Python AI observability with OpenTelemetry and compatible model/framework instrumentation. Use for AI task/tool/retrieval traces, usage and latency signals, export/privacy configuration and operational diagnosis; keep the chosen backend and quality evaluation separate.