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Voice AI agents have come a long way. What used to take a bunch of custom tools and webhook wiring can now be done much faster using MCP. In this video, I show how to build a simple AI receptionist that: - Answers calls - Collects customer info - Checks availability - Books appointments into Google Calendar - Saves the customer record into Google Sheets - Sends a confirmation email Here is the flow, step by step: 1. Set up the voice agent in Vapi - Use GPT 4.1 mini for lower cost and faster responses - Make the assistant speak first - Add a system prompt with identity, tone, and tool instructions 2. Connect Vapi to n8n using MCP - Add an MCP Server Trigger in n8n - Build your tools as sub workflows - In Vapi, create an MCP tool and paste the MCP server URL - Add an Authorization header using your n8n API key 3. Create the sub workflows (these become your tools) - Get user - Search the Google Sheet by email - If not found, return “new client” so the agent knows to create them - Create user - Append a new row with name, phone, email, and call date - Get available slots - Pull calendar events for the requested date - Return busy times between business hours (example 9:00 AM to 5:00 PM) - Book appointment - Create the Google Calendar event - Update the user row with service type and appointment time - Send a confirmation email 4. Let the model fill tool parameters automatically - In the MCP trigger inputs, use “defined automatically by the model” - This avoids extra agents and reduces latency and cost 5. Test the full experience end to end - Call the number - Book an appointment - Confirm the data shows up in Sheets and Calendar - Confirm the email is sent If you want to build this exact setup, I included the resources and workflows in the description. Subscribe for more tutorials on voice AI agents, MCP, and real automations you can deploy. https://youtu.be/IiTV70i-2zA?si=IQAZEllSWNTqoPyH

How are you my friend I am from Yemen and we suffer from wars and problems and my financial situation is very difficult can you help me buy food for my family to eat humanitarian service

While working with a few clients, I noticed something odd: people repeat themselves way less on calls than on chat or email. On chat, they re-explain context. On email, they forward threads. On calls, they just move forward. This became obvious while testing different call setups recently and spending some time with Feather Ai. When context carried over properly between turns, calls felt… lighter. Less friction. Made me realize the real value isn’t “AI on calls” - it’s removing the back-and-forth humans hate. Curious if others have seen this too. Where do you see the most friction today: calls, chat, or email?