Let agents find US doctors with checkable evidence over MCP
A developer built a remote MCP server with 1.2M US physicians, Medicare procedure volumes and facts from hospital sites.
Real estate · Do this yourself
✅ Shared on GitHub; commenters favor the hybrid approach.
Real estate agents, investors, property managers and renters or buyers.
Extract {budget, location, timeline_months, intent} as JSON from this enquiry. Do not score it.Opens the AI with this guide and the prompt filled in.
Pricing idea: If you help other agents, charge per listing package (for example $50–200) or a monthly fee.
Ideas, not promises. Results vary.
It extracts budget, location, timeline and intent, scores and routes in n8n, matches properties, writes follow-ups, notifies the sales team on WhatsApp and nurtures low-intent leads.
Shared on GitHub; commenters favor the hybrid approach.
Keeping weights in n8n means a model change will not silently change scores.
A developer built a remote MCP server with 1.2M US physicians, Medicare procedure volumes and facts from hospital sites.
A hotel analyst built an n8n workflow to check housekeeping staffing needs with data when staff call in sick.
A builder made an n8n workflow where AI drafts customer email replies and you approve, edit or reject them.
A hobbyist spent months getting ChatGPT and Claude to write LDraw, the instruction language for LEGO CAD models, and open-sourced the tooling.
Five real AI agent use cases each morning, each with a recipe and a source. Plus a Sunday roundup. Free.