I just shared a workflow with stakeholders @sparkfinance — get data insights directly through @claudeai + @Dune MCP, no analyst needed. 🧵 ➢ The workflow: 3 steps 1. Identify the right dashboard Match the stakeholder's question to the relevant dashboard. Each dashboard sits on top of pre-validated intermediate tables I maintain — two reasons this matters: • Accuracy — data is validated before AI even touches it • Cost — pre-aggregated tables consume far fewer Dune credits than pulling raw on-chain data 2. Drop the dashboard URL into Claude No SQL. No data wrangling. Just a prompt. 3. Keep drilling down in the same chat The tables are granular enough for follow-up questions without starting over. ➢ Why dashboards? Raw tables are slow and error-prone. Dashboards are pre-validated, pre-aggregated, and always fresh. You get accurate data instantly — and the AI can reason on top of it without guessing. ➢ Example prompt for SparkLend weekly analysis: Please use Dune MCP to pull the latest data from these dashboards. Summarize key changes over the past 7 days: TVL, utilization, whale activity, top risks. One message. One minute. Done. ➢ Somewhere along the way, I became the AI's data engineer. I build the tables. I validate the logic. I maintain the pipelines. The AI does the analysis. Honestly? I'm fine with that. That's what frees me up to learn more, ask better questions, and do the work that actually matters. Setup Dune MCP: Thanks @Dune for building this — making it genuinely easy to bring AI into on-chain data workflows. 🙏 cc @blstamm11 @kdotkrisp @fr0zensun