sales-mentiondrop

MentionDrop Platform Help

Step 1 — Gather context

If references/learnings.md exists, read it first for accumulated platform knowledge.

Ask the user:

1. What do you need help with?
   • A) Setting up keyword monitoring (choosing keywords, adding context)
   • B) Reducing noise / too many irrelevant mentions
   • C) Understanding AI summaries, sentiment, or relevance scores
   • D) Webhook or Slack integration setup
   • E) Using the API to pull mentions into another tool
   • F) Plan limits and upgrade decision
   • G) Something else — describe it

1. Which plan are you on?

   • A) Free (1 keyword)
   • B) Starter ($29/mo — 5 keywords)
   • C) Pro ($59/mo — 20 keywords)
   • D) Not sure / haven't signed up yet

2. What's your goal?

   • A) Brand monitoring (track what people say about us)
   • B) Competitor monitoring (track competitor mentions)
   • C) Lead generation (find people asking for solutions like mine)
   • D) PR / crisis detection (catch negative mentions fast)

If the user's request already provides context, skip to Step 2.

Step 2 — Route or answer directly

• Social listening strategy or tool comparison → /sales-social-listening [question]
• Developer-first monitoring with MCP + API on all plans → /sales-octolens [question]
• Free Reddit/HN monitoring with more keywords → /sales-f5bot [question]
• Multi-platform monitoring with AI filtering → /sales-syften [question]
• Audience intelligence (where audiences spend attention) → /sales-sparktoro [question]
• Reddit lead generation with intent scoring → /sales-buska [question]

Otherwise, answer directly from the platform reference below.

Step 3 — MentionDrop platform reference

Read references/platform-guide.md for the full platform reference — capabilities, pricing, data model, API details, integration recipes, and workflow examples.

Answer the user's question using only the relevant section. Don't dump the full reference.

Step 4 — Actionable guidance

Focus on the user's specific situation:

1. Noise reduction — add keyword context to help MentionDrop distinguish your brand from common words, use sentiment filters to focus on negative or positive mentions, adjust relevance scoring thresholds
2. Keyword strategy — use exact brand name, product names, and competitor names; add context descriptions so the AI understands what you mean by ambiguous terms
3. Alert routing — use Slack for real-time team awareness, webhooks for CRM/automation pipelines, email digests for daily review
4. API integration — the API is read-only; use it to pull processed mentions into dashboards or data pipelines; filter by keyword, sentiment, content_type, and date range
5. Plan selection — Free is fine for testing; $29/mo covers most solo founders (5 keywords); $59/mo for teams monitoring multiple brands/competitors

If you discover a gotcha or tip not covered in references/learnings.md, append it there.

Gotchas

Best-effort from research — review these, especially items about plan-gated features and pricing that may change.

• API is read-only. You can pull mentions but can't create keywords or manage settings via API. Use the dashboard for configuration.
• Free plan is extremely limited. Only 1 keyword — useful for testing but not for real monitoring. Upgrade to $29/mo for 5 keywords.
• Keyword context is critical for noise reduction. MentionDrop uses AI to filter relevance, but ambiguous brand names (e.g., "Mercury", "Notion") need explicit context like "Mercury is a banking app for startups" to avoid false positives.
• Web monitoring depends on Ahrefs Firehose. Coverage is broad but not exhaustive — niche forums or gated communities may not appear. Reddit coverage is direct.
• Webhook payload format is not fully documented. Test with webhook.site first to see the actual payload structure before building integrations.
• No native Zapier/Make app. Use webhooks to trigger Zapier/Make/n8n workflows. For iPaaS-native monitoring, consider Brand24 or Octolens.
• Detection time averages ~4 minutes. Faster than Google Alerts (2-3 days) but not truly real-time. For sub-minute alerts, consider Syften or Octolens.
• No historical data backfill. Monitoring starts from when you add a keyword — no retroactive mention discovery.

Related skills

• /sales-social-listening — Social listening strategy across all platforms — tool comparison, monitoring setup, competitive intel, crisis detection
• /sales-octolens — Octolens — developer-first social listening with API/MCP on all plans from $25/mo
• /sales-f5bot — F5Bot — free Reddit/HN/Lobsters keyword monitoring with 200 free keywords
• /sales-syften — Syften — AI-filtered keyword monitoring across Reddit, HN, X, Bluesky, GitHub, sub-minute alerts
• /sales-buska — Buska — AI social listening for lead gen across 30+ platforms, intent scoring, MCP server
• /sales-brand24 — Brand24 — affordable social listening with Storm Alerts and MCP server
• /sales-do — Not sure which skill to use? The router matches any sales objective to the right skill. Install: npx skills add sales-skills/sales --skill sales-do

Examples

Example 1: Set up brand monitoring for a SaaS product

User says: "I just launched a project management tool called TaskFlow and want to know when people mention it online" Skill does:

1. Reads platform guide for keyword setup and context configuration
2. Creates keyword "TaskFlow" with context: "TaskFlow is a project management SaaS tool for remote teams"
3. Adds competitor keywords: "Asana", "Monday.com", "ClickUp" for competitive monitoring
4. Configures Slack for real-time alerts and email for daily digests
5. Recommends $29/mo plan for 5 keywords covering brand + top competitors Result: Brand monitoring live within minutes, AI-summarized alerts with sentiment

Example 2: Pull MentionDrop mentions into a dashboard via API

User says: "How do I get my MentionDrop mentions into a Google Sheet or dashboard?" Skill does:

1. Reads API reference for authentication and endpoint details
2. Shows how to create an API key in the dashboard Settings
3. Provides cURL example: GET /api/v1/mentions?sentiment=negative&limit=50
4. Builds a Python script to fetch mentions and write to Google Sheets via gspread
5. Sets up a cron job or n8n workflow to run the script hourly Result: Automated pipeline from MentionDrop mentions to spreadsheet dashboard

Example 3: Too many irrelevant mentions

User says: "I'm monitoring 'Mercury' but getting tons of mentions about the planet and the car brand" Skill does:

1. Explains keyword context feature — add a description like "Mercury is a banking platform for startups"
2. Recommends using sentiment filter to focus on relevant business mentions
3. Suggests adding more specific keywords: "Mercury banking", "Mercury startup bank"
4. Notes that the AI relevance scoring should deprioritize off-topic mentions over time Result: Cleaner mention feed focused on the actual brand

Troubleshooting

Mentions arriving late or missing

Symptom: You see a Reddit post about your brand but MentionDrop didn't catch it, or it appeared hours later Cause: Average detection time is ~4 minutes for indexed sources, but niche forums or low-traffic subreddits may take longer. Some web sources depend on Ahrefs Firehose crawl frequency. Solution: Check if the source is within MentionDrop's coverage (web via Ahrefs + Reddit). For niche forums, consider adding ForumScout or Syften as supplementary monitoring. For Reddit-specific speed, Syften offers sub-minute alerts.

Webhook not delivering payloads

Symptom: Webhook URL is configured but your endpoint never receives data Cause: Endpoint may be returning non-2xx status, URL may be incorrect, or no new mentions match your filters Solution: Test with webhook.site first to verify MentionDrop sends payloads. Check that your endpoint accepts POST requests and returns 200. Lower any filters temporarily to trigger a test mention. Verify the webhook URL in your MentionDrop dashboard settings.

AI summaries seem inaccurate

Symptom: Sentiment says "positive" but the mention is clearly negative, or the summary misses the key point Cause: AI summarization (Gemini Flash-Lite) can struggle with sarcasm, industry jargon, or multi-language content Solution: Use summaries as a quick filter, not a final judgment. Click through to the original mention for important conversations. If sentiment is consistently wrong for your industry, rely more on relevance scoring and manual review for high-impact mentions.