Answer patient enquiries for a clinic by chat
An n8n and Gemini agent receives patient chat messages, keeps context, uses clinic info from Google Sheets and responds to enquiries.
A team connected a Gmail MCP server so Claude Code reads the inbox, labels messages and drafts replies, plus calendar prep and reports.
A team connected a Gmail MCP server so Claude Code reads the inbox, labels messages and drafts replies, plus calendar prep and reports.
The team wired a Gmail MCP server into Claude Code, which has a shell, a file system and MCP access. It reads the founder's inbox, applies labels and drafts replies. They also set up Calendar, Slack and Notion MCP servers for a 'prep me for tomorrow' command that collects context for each meeting.
The authors call founder inbox triage the most useful non-code job Claude Code does. They give no metrics. Each extra SaaS tool needs its own MCP server, and browser work and unattended runs have limits.
Expect an MCP tax: each tool (Gmail, Calendar, Slack, Notion) needs its own server installed and authenticated, and that setup is painful across a team. The authors also compete with Claude Code.
Using my connected Gmail MCP server, read the last [number] emails in my inbox and label each as [e.g. Urgent, Reply, FYI, Ignore]. Draft replies for the Urgent and Reply ones in my voice, saved as drafts. Do not send or delete anything; show me a summary table of labels and drafts for approval.
An n8n and Gemini agent receives patient chat messages, keeps context, uses clinic info from Google Sheets and responds to enquiries.
An n8n workflow has AI classify web-form jobs by type, urgency and summary, create an Airtable record, reply instantly and flag uncertain cases.
Lindy agents summarized meeting notes, sent action items to teammates, suggested knowledge-base replies on support tickets, and handled follow-up emails and calendar updates.