search-first

To find existing libraries before writing custom code, search npm, PyPI, GitHub, and internal MCPs to identify reusable solutions. This structured research evaluates maintenance, docs, licenses, and compatibility to prevent reinventing the wheel and accelerate delivery.

The best way to evaluate existing libraries is scoring candidates against criteria like maintenance activity, documentation quality, licenses, and compatibility. This structured decision-making process yields actionable integration planning outcomes for your new feature requirements.

To search internal MCPs for reusable patterns, perform a structured upfront research scan across your internal environments alongside npm, PyPI, and GitHub. This identifies existing components to adopt or extend before writing custom code.

You need upfront research before coding when starting a new feature to quickly identify reusable components. It prevents duplication by enforcing structured searches across npm, PyPI, GitHub, and internal MCPs to find existing solutions.

Yes, upfront library research works with both Python and JavaScript projects by scanning PyPI and npm respectively. It evaluates candidates across both ecosystems using criteria like maintenance, compatibility, and licenses to identify reusable solutions.

Reinventing the wheel happens when teams skip evaluating existing tools. Integration planning prevents it by enforcing upfront research to score candidates on maintenance and compatibility, yielding actionable outcomes that guide teams to adopt reusable solutions.