AI-Assisted Debugging To Uplevel Smart Home Heating

Harshal
1 day ago
6 min read

Combined Cursor, n8n, Lovable, and more to diagnose and fix Tado API rate limits in Home Assistant

For several hours daily my smart home heating controls stopped working: some rooms went cold, others overheated. I used n8n, Home Assistant, AI coding assistants (including Cursor), and a BYO API usage dashboard to see why my smart home pinged the heating system 20,000 times a day to burn through rate limits. This helped redesign the automations, and stay warm and within both Tado's and n8n's quotas.

You need about 6 minutes to read this.

Video walkthrough of the project:

https://www.youtube.com/watch?v=lmThFJOFPx8

Learnings In AI Assisted Debugging

Here are the principles that helped; the rest of the post shows how they played out in this setup.

Persistence and audit trail: Storing samples and sending telemetry into a few sinks (e.g. tables, sheets, dashboards) gives you an audit trail. Tracing trends over time makes it obvious when usage changes after upgrades or new devices. In this post I used an n8n datatable, Google Sheets, and a Lovable dashboard.
Visualization: Seeing usage over time (charts, dashboards) makes it much easier to check that a change actually improved things. Below I use QuickChart, a custom API usage dashboard, and an n8n Insights-style view.
Runaway automations: When many automations or integrations hit the same API, a few can dominate usage and blow the limit. Finding which flows or triggers are the heaviest is half the fix. In my case, one workflow would have consumed over 100% of my monthly execution quota.
Sampling over full logging: You often do not need every event. Regular snapshots (e.g. every few hours) gave almost all the insight while keeping extra quota use low.
Knowing the limits of low-code: Low-code and no-code tools get you far, but reliability and insight often depend on understanding their limits and adding your own logging, alerting, and visibility.

Problem Context

We were spending a lot on gas heating. So, I used Home Assistant and Tado to make my Irish house heating smart. I had already cut heating usage by 52% with room-by-room automations that turned heating on/off in rooms by detecting presence. After many months of bliss, Tado changed its API rate limits. I started getting rate limited. For a few hours daily, none of my heating controls worked. This is a story of how I used Cursor, n8n, Lovable, and more to diagnose and fix the problem.

First hard part was diagnosis. I had many flows across rooms and purposes. I needed to know which automations were consuming API calls, which to turn off or optimize, and whether I was moving in the right direction after changes.

Node-RED flows in Home Assistant showing many automations across rooms: heating, hot water, presence, motion, with rate-limiting nodes.

Diagnosis

I wanted to understand the limit and redesign the logic.

I was hitting the cap: 20,000 API calls per day. With some automations disabled I was around 10K to 17K. Hot water and attic heating turned out to be major contributors.

I exported the automations from Node-RED and Home Assistant into Cursor and used AI assistance to brainstorm and streamline the logic. That produced a large flowchart using Mermaid Diagram. I used this as the specification/PRD and iterated over it. Then, asked AI to implement the revised automation in Home Assistant.

Flowchart of hot water automation with triggers, priority-based decisions for heating on/off, and rate-limiting conditions such as 'Did hot water turn ON/OFF within 3 minutes?' and T1 minute exclude time.Mermaid Chart - Create complex, visual diagrams with text.

Checking API Usage in Real Time

I needed a way to see how many Tado API requests were left and how usage behaved over time. I built an n8n workflow. It had a one-time branch for Tado auth and token storage, and a recurring branch that ran on a schedule (every 3 hours in the final setup; I had started with every 10 minutes). The recurring flow got the stored refresh token, refreshed the access token, called the Tado API to read rate limit info, parsed it, and saved each sample to an n8n datatable.

Each check used one of the 20,000 daily Tado API calls. I was doing 144 checks a day. So I reduced the check frequency to balance visibility with quota. The datatable made it possible to see patterns over time. I could see that the counter reset around 12 noon, which explained why heating often failed in the late morning.

n8n workflow with one-time auth flow and recurring flow that runs every few hours to probe Tado rate limits and save results to an n8n datatable.

Visualizing Usage: Charts and Telegram

Viewing the datatable alone was not enough as I preferred a visual for a quick overview. I added a n8n workflow that ran on a daily schedule. It read the n8n datatable and used QuickChart to generate a chart of remaining requests over the course of the day. It sent the chart as a photo to Telegram. I also had periodic text messages with the percentage of requests remaining so I could see at a glance whether I was close to the limit. It also exported the datatable data to Google Sheets to make it easy to do further analysis.

n8n workflow with Schedule Trigger, data from datatable/sheet, Sort, Limit, conditional for many data points, Aggregate, QuickChart, and notify on Telegram (send photo and text).

The QuickChart node in n8n hit URL length limits when using GET requests above a 250+ data points. The fix was to use a POST-based approach (e.g. a quickchart_post-style node) so the chart config is not sent in the URL. This would've required additional time to setup HTTP Request nodes, so I instead added data sampling (e.g. picking every 3rd data point).

Telegram chat with automated Tado API rate limit updates: percentage remaining, exact count out of 20,000, and timestamps; plus an embedded line chart showing remaining requests over several days with a sawtooth pattern and reset near noon.

Feeding the API Usage Dashboard

I already had a Lovable-built API usage dashboard that tracks multiple providers. I wanted Tado daily usage to appear there.

So I added a webhook-triggered n8n workflow: when the dashboard called the webhook, the workflow got rows from the datatable for the day, computed the maximum usage (or similar metric) for that day, formatted the response, and responded to the webhook.

n8n workflow with webhook trigger that gets max usage for the day from the datatable and responds to the webhook for the dashboard.

The dashboard then pulled that data and displayed Tado usage and cost trends alongside the other APIs.

API Usage Dashboard widget showing Tado usage and cost trends: bar chart of daily API calls over time, with total usage and cost.

Hitting n8n Usage Limits

Checking Tado rate limits every 10 minutes meant the "Check Tado API Rate Limits" workflow would have consumed 170% of my monthly usage quota if I had left it running at that frequency. I got an alert from n8n that I was reaching my usage limit. So, I used the n8n Insights dashboard to see execution counts per workflow. I then reduced how often the Tado check runs and tuned other workflow triggers so I stayed within both Tado's and n8n's limits.

n8n Insights or Analytics dashboard showing workflow execution counts, with the Tado rate limit check workflow highlighted and its high execution count visible.

Findings for Tado and Home Assistant

Rate limit reset time: Seeing the datatable and charts made it obvious the Tado counter resets around noon. That explained why heating failed in the late morning: the quota was exhausted until the next reset.
Monitoring consumes quota: Each rate-limit check uses one Tado API call. Balance check frequency against how much visibility you need so you do not burn the limit on monitoring alone.
Two kinds of limits: Tado's daily API cap and n8n's monthly execution cap both matter. The n8n Insights dashboard showed which workflow was using most executions; I reduced that workflow's frequency and kept the rest of the automations within both limits.

The setup is still mostly n8n and Home Assistant. What changed was adding my own visibility: rate-limit checks, charts, and the API dashboard. That is the difference between vibe engineering and making it reliable.