Hey folks! I’m working on a cool project using n8n to build a bot that books appointments. Here’s what I want it to do:
When a message comes in, check if the person is in our Airtable database
If they’re not there or if some info is missing, the bot should chat with them to get the details
Then update or add the info to Airtable
I’ve got the chat part kind of working, but I’m stuck on updating Airtable. Right now I’m using the AI Agent node with Airtable tools, but it’s not doing the trick.
Has anyone tried using MCPs for something like this? I’ve heard they’re good but I’m not sure how they work or if they’d be better for this.
I’ve actually tackled a similar project recently, and I can share some insights that might help you out. Instead of using the AI Agent node with Airtable tools, I found more success by leveraging n8n’s native Airtable nodes. They offer better control and reliability for CRUD operations.
For the chat functionality, I’d recommend looking into integrating a dedicated conversational AI service like Dialogflow or Rasa. These can handle the nuanced interactions needed to gather missing info more effectively than a basic chatbot.
As for MCPs (Multi-Channel Platforms), they can indeed be beneficial, especially if you’re planning to expand your bot to multiple channels in the future. However, for your current scope, it might be overkill. Focus on getting the core functionality solid first.
One trick that worked well for me was implementing a caching mechanism to reduce Airtable API calls. This improved performance significantly, especially when dealing with frequent checks against the database.
Hope this helps point you in the right direction. Good luck with your project!
yo harry, ive tried similar stuff. try using n8n’s http request to hit airtable api directly. for chat, check dialogflow or botpress. mcp’s might be overkill rn. keep it simple. good luck!
Having worked on similar projects, I can offer some advice based on my experience. Instead of the AI Agent node, consider using n8n’s HTTP Request nodes to interact directly with Airtable’s API. This approach gives you more granular control over the data operations.
For the conversational aspect, I’ve found success implementing a state machine within n8n. It allows you to manage the flow of the conversation and collect necessary information step by step. You can use Switch nodes to handle different conversation states.
Regarding MCPs, while they’re powerful, they might be unnecessary for your current scope. Focus on perfecting the core functionality first.
A tip: implement error handling and logging throughout your workflow. It’ll save you countless hours of debugging as your project grows in complexity. Good luck with your appointment scheduling bot!