mcpField
A
apify-brand-reputation-monitoring

apify-brand-reputation-monitoring

github.com/apifyMarketing & growth2.5k2.5k installsUpdated 1w ago
Repository

About apify-brand-reputation-monitoring

A skill for collecting reviews, ratings, and brand mentions across platforms with Apify Actors. It helps teams monitor reputation by selecting an Actor for a data source, running it, and reviewing or exporting results.

Key features

  1. 01Select Apify Actors by data source
  2. 02Fetch Actor input schemas dynamically
  3. 03Run monitoring and summarize results
  4. 04Display results or export as CSV or JSON

Use cases

  1. 01Collect Google Maps business reviews and ratings
  2. 02Monitor brand mentions on social platforms
  3. 03Gather hotel, attraction, or restaurant reviews

MCP support

Uses mcpc to connect to mcp.apify.com and retrieve details and input schemas for selected Actors, which the skill then runs.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/apify/agent-skills --skill apify-brand-reputation-monitoring

After installing, type/apify-brand-reputation-monitoringin Claude Code to confirm it loaded.

Bundled filesreference/scripts/run_actor.js

Details

From the project README

Brand Reputation Monitoring

Scrape reviews, ratings, and brand mentions from multiple platforms using Apify Actors.

Prerequisites

(No need to check it upfront)

  • .env file with APIFY_TOKEN
  • Node.js 20.6+ (for native --env-file support)
  • mcpc CLI tool: npm install -g @apify/mcpc

Workflow

Copy this checklist and track progress:

Task Progress:
- [ ] Step 1: Determine data source (select Actor)
- [ ] Step 2: Fetch Actor schema via mcpc
- [ ] Step 3: Ask user preferences (format, filename)
- [ ] Step 4: Run the monitoring script
- [ ] Step 5: Summarize results
Step 1: Determine Data Source

Select the appropriate Actor based on user needs:

User NeedActor IDBest For
Google Maps reviewscompass/crawler-google-placesBusiness reviews, ratings
Google Maps review exportcompass/Google-Maps-Reviews-ScraperDedicated review scraping
Booking.com hotelsvoyager/booking-scraperHotel data, scores
Booking.com reviewsvoyager/booking-reviews-scraperDetailed hotel reviews
TripAdvisor reviewsmaxcopell/tripadvisor-reviewsAttraction/restaurant reviews
Facebook reviewsapify/facebook-reviews-scraperPage reviews
Facebook commentsapify/facebook-comments-scraperPost comment monitoring
Facebook page metricsapify/facebook-pages-scraperPage ratings overview
Facebook reactionsapify/facebook-likes-scraperReaction type analysis
Instagram commentsapify/instagram-comment-scraperComment sentiment
Instagram hashtagsapify/instagram-hashtag-scraperBrand hashtag monitoring
Instagram searchapify/instagram-search-scraperBrand mention discovery
Instagram tagged postsapify/instagram-tagged-scraperBrand tag tracking
Instagram exportapify/export-instagram-comments-postsBulk comment export
Instagram comprehensiveapify/instagram-scraperFull Instagram monitoring
Instagram APIapify/instagram-api-scraperAPI-based monitoring
YouTube commentsstreamers/youtube-comments-scraperVideo comment sentiment
TikTok commentsclockworks/tiktok-comments-scraperTikTok sentiment
Step 2: Fetch Actor Schema

Fetch the Actor's input schema and details dynamically using mcpc:

export $(grep APIFY_TOKEN .env | xargs) && mcpc --json mcp.apify.com --header "Authorization: Bearer $APIFY_TOKEN" tools-call fetch-actor-details actor:="ACTOR_ID" | jq -r ".content"

Replace ACTOR_ID with the selected Actor (e.g., compass/crawler-google-places).

This returns:

  • Actor description and README
  • Required and optional input parameters
  • Output fields (if available)
Step 3: Ask User Preferences

Before running, ask:

  1. Output format:
    • Quick answer - Display top few results in chat (no file saved)
    • CSV - Full export with all fields
    • JSON - Full export in JSON format
  2. Number of results: Based on character of use case
Step 4: Run the Script

Quick answer (display in chat, no file):

node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT'

CSV:

node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT' \
  --output YYYY-MM-DD_OUTPUT_FILE.csv \
  --format csv

JSON:

node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT' \
  --output YYYY-MM-DD_OUTPUT_FILE.json \
  --format json
Step 5: Summarize Results

After completion, report:

  • Number of reviews/mentions found
  • File location and name
  • Key fields available
  • Suggested next steps (sentiment analysis, filtering)

Error Handling

APIFY_TOKEN not found - Ask user to create .env with APIFY_TOKEN=your_token mcpc not found - Ask user to install npm install -g @apify/mcpc Actor not found - Check Actor ID spelling Run FAILED - Ask user to check Apify console link in error output Timeout - Reduce input size or increase --timeout

Comments

Markdown supported