Use Google Maps Reviews Scraper with Hermes Agent
Connect Hermes Agent to structured google maps place reviews, ratings, dates, and owner responses so your agent can research, monitor, summarize, and take action without manual copy-paste.
Quick answer
Give Hermes Agent a structured google maps place reviews, ratings, dates, and owner responses feed, then ask it to turn the records into summaries, alerts, ranked opportunities, briefs, or follow-up tasks. This workflow is for people who want an agent to collect outside evidence, preserve source URLs, and produce useful decisions instead of only chatting from stale context.
When you need this
- ✓You need repeatable google maps place reviews, ratings, dates, and owner responses without manually copying pages into chat.
- ✓You want Hermes Agent to analyze google maps place reviews, ratings, dates, and owner responses and produce decisions, not just raw rows.
- ✓You need the workflow to run from chat, cron, a terminal prompt, or a scheduled Hermes task.
- ✓You want a reusable pattern for giving Hermes Agent fresh external data with source links and caveats.
What you can do with it
How to use it with Hermes Agent
- 1
Create an account on the data platform
Use the setup link below to create an account. It gives you $5 of credit, which is more than enough to test this Google Maps Reviews Scraper workflow before you spend anything.
Create account and get $5 credit - 2
Add your API token to Hermes
Store the API token as APIFY_TOKEN in the Hermes environment. Do not paste the token into prompts, screenshots, chat messages, or public files.
- 3
Run the data collection from Hermes
Ask Hermes to call the connected data API or MCP tool with your target input, for example: reviews for top-rated dentists in Austin with rating, text, date, owner response, and source URL.
- 4
Make Hermes reason over the dataset
Tell Hermes the output format you want: ranked list, monitoring alert, CSV-ready export, executive brief, follow-up tasks, or a saved research note.
Recommended data API
Use Google Maps Reviews Scraper (`compass/Google-Maps-Reviews-Scraper`) as the collection layer. It gives your agent structured google maps place reviews, ratings, dates, and owner responses without making you build and maintain a scraper from scratch.
Copy-paste prompt
You are Hermes Agent. Use Apify Actor compass/Google-Maps-Reviews-Scraper (Google Maps Reviews Scraper) to collect data for this request:
reviews for top-rated dentists in Austin with rating, text, date, owner response, and source URL
After the Actor run finishes, inspect the dataset and return:
1. a short executive summary
2. the 10 most important records with source URLs
3. patterns, anomalies, or opportunities
4. recommended next actions
5. any data-quality caveats, missing fields, duplicates, or blocked recordsAPI example
# Run the Apify Actor, then let Hermes analyze the dataset
curl -s -X POST \
"https://api.apify.com/v2/acts/compass~Google-Maps-Reviews-Scraper/runs?token=***" \
-H "Content-Type: application/json" \
-d '{
"query": "reviews for top-rated dentists in Austin with rating, text, date, owner response, and source URL",
"maxItems": 100
}'
# In Hermes Agent, ask:
# "Read the latest dataset for Google Maps Reviews Scraper and turn it into an action plan."MCP / tool prompt example
# Hermes prompt after connecting the data API MCP
Use the Actor "Google Maps Reviews Scraper" (compass/Google-Maps-Reviews-Scraper) to collect:
reviews for top-rated dentists in Austin with rating, text, date, owner response, and source URL
Then:
1. remove duplicates and low-quality rows
2. summarize the strongest patterns
3. preserve source URLs for every claim
4. create a prioritized next-action list
5. save the source dataset link in the final answerCommon failure modes
The dataset is too large for the context window
Have Hermes sample, aggregate, or write the dataset to a file before summarizing. Do not paste thousands of raw records into a prompt.
Inputs are too broad
Start with a narrow target such as `reviews for top-rated dentists in Austin with rating, text, date, owner response, and source URL` and increase maxItems only after the workflow produces useful output.
The model treats collected data as fully verified truth
Ask Hermes to label uncertainty, preserve source URLs, separate raw observations from recommendations, and call out missing or inconsistent fields.
Scheduled runs become noisy or expensive
Run manually first, then schedule daily or weekly only after the output proves it can drive a real decision or workflow.
Alternatives
- •Use the official platform API when it has the exact endpoint you need and the limits are acceptable.
- •Use Hermes browser automation for one-off research, but use the managed data API for repeatable collection and scheduling.
- •Use a custom scraper only when the managed data API cannot capture the fields, permissions, or compliance constraints you need.
FAQ
Can Hermes Agent use Google Maps Reviews Scraper?
Yes. Hermes can call the connected data API or MCP tool, then reason over the dataset produced by Google Maps Reviews Scraper.
Do I need to write scraper code?
Usually no. The point of this pattern is to let the collection layer return structured records while Hermes handles reasoning, QA, summarization, and follow-up actions.
Should this workflow be scheduled?
Schedule it only after a manual run proves the output is valuable. Hermes cron jobs are useful for monitoring, but bad inputs at scale create noisy reports.