Learn / The context behind the tools
Make your data useful to your agents.
A short learning path for people building AI-assisted operations. Understand the foundation, define the access, then investigate a real question.
Start with the question, not the integration.
Read in order if you are new to shared data and MCP. Already have a GitHub repository connected? Go straight to the practical investigation guide. Each guide includes examples, boundaries and a concrete next step.
Raw data vs views: keep the evidence, shape the answer
Understand source records, imported tables and task-specific views. Work through a GitHub issue example and learn how to avoid misleading summaries.
Business context and MCP: give agents evidence they can use
A practical guide to business context for AI agents: define the question, discover the data, scope access and ask for a traceable answer through MCP.
Three useful GitHub questions to ask with Lake
Prepare an issue review queue, inspect open pull requests and investigate failed GitHub Actions runs using Lake's imported tables and read-only MCP tools.
Start with a stronger foundation
Build on what
your business knows.
Your next step starts with Lake.