Python Best Practices

Python Best Practices is a Developer Tools skill built for teams that want their AI agents to write, review, and refactor Python code the way a senior engineer would — not just code that runs, but code that holds up in production. It encodes concrete guidance across the areas most Python teams struggle to keep consistent: type hints and static typing discipline, correct use of async/await for I/O-bound workloads, testing conventions built around pytest (fixtures, parametrization, mocking), packaging structure (pyproject.toml, dependency pinning, src-layout), and performance optimization techniques like avoiding needless copies, choosing the right data structures, and profiling before optimizing. When an AI agent has this skill available, code review, code generation, and refactoring requests get grounded in these Python development patterns automatically, instead of relying on whatever style happened to be in the model's training data.

On BusinessMCP, this skill is connected once to your hosted MCP server and immediately becomes available to every AI agent your organization uses — Claude, GPT, Gemini, or any other model-agnostic client — through the same /api/mcp endpoint, authenticated with your company's mcph_* Bearer key. That means engineering leads don't need to re-teach Python conventions to every coding assistant separately; the best-practices logic lives in one governed place, and any agent that queries your MCP server inherits the same standards for type hints, async patterns, pytest testing, packaging, and performance. Because BusinessMCP is cookieless and GDPR-friendly by design, this holds true even in regulated environments where you can't rely on client-side tracking or per-tool logins to enforce policy.

Teams typically pair this skill with their existing CI and code-review workflow: an agent reviewing a pull request checks for missing type hints, flags blocking calls inside async functions, suggests pytest fixtures instead of duplicated setup code, and calls out packaging or dependency issues before they reach main. Because the skill sits inside the same unified MCP layer as your other tools, databases, and revenue data, an agent can also correlate code-quality signals with business context — for example, flagging that a slow, un-optimized async endpoint corresponds to a customer-facing feature showing up in your BI dashboard's latency or churn metrics, and surfacing that connection without extra integration work.

This skill is a strong fit for any organization standardizing Python development across multiple repositories or contractors, onboarding new engineers who need a consistent mental model for testing and typing, or preparing legacy scripts for a package release. It's equally useful for solo developers who want an AI pair-programmer that defaults to idiomatic, testable, type-safe Python rather than quick-and-dirty scripts. Because it's exposed through the same hosted MCP server as your other business tools, there's no separate plugin to install per AI client — connect it once inside BusinessMCP, and every agent you use going forward speaks the same Python best-practices language, with usage visible alongside the rest of your business intelligence dashboard.