MentionDrop MCP

How are you handling the massive scale of 8 billion pages scanned daily? Is your AI summary and sentiment analysis based on a specific NLP library or a custom implementation?

How does it handle false positives or really low-quality mentions, like random forum spam or machine-generated content? Curious how strict the filtering is before something actually lands in your dashboard.

how does the sentiment scoring hold up across languages that use sarcasm or idioms a lot, like portuguese or japanese?

the "nothing auto-posted, drafts for review" part is what separates this from the stuff that gets brands in trouble. i've been doing something similar by hand across a few communities and the actual bottleneck is never finding mentions, it's triaging which ones are worth a human reply versus noise. curious how the triage decides what's worth surfacing versus what gets buried, is that tunable per source or is it one global scoring model right now

Could this be used to monitor product launches like Product Hunt and automatically surface high-priority conversations?

How does the AI handle sarcasm or context-heavy mentions where sentiment isn't obvious, and does that affect the action suggestions it makes?

LOVE THIS PRODUCT! MentionDrop was already a really great at filtering out the noise in social listening and raising what matters, but the MCP takes it to another level! My Hermes agent now drafts replies in my voice that I pick through and post on each day. I’m finding great success with GTM, and MentionDrop plays a key part in that success.

The AI summaries actually capture nuance better than I expected, especially the suggested actions which feel useful rather than generic. Setup took a couple minutes and it picked up mentions from forums I forgot even existed.

The MCP angle here is smart, pulling this into the agent's workflow instead of another dashboard to check. Question on the foreign-language sites part of the pitch - sentiment scoring is already tricky in English with sarcasm and industry slang, how does accuracy hold up once you're scoring sentiment on a mention that's been through translation first? That seems like the place this could quietly go wrong without anyone noticing until a genuinely bad mention gets triaged as neutral.

love that you name the limits out loud (no X, no 'whole internet') instead of overclaiming. does the agent learn which sources matter per brand over time?

Hey Product Hunt! 👋I'm Marcos, maker of MentionDrop.MentionDrop watches the places where buyers actually talk: Reddit, Google News, search results, and selected public web pages. AI triages every mention: summary, sentiment, relevance, and whether to reply, share, monitor, or ignore.Today's launch gives your AI agent that same view. Connect Claude, Cursor, or Windsurf with an API key and ask:"What should I pay attention to today?""Find competitor complaints from the last 7 days""Draft a reply for the most urgent mention"11 tools, read-first design. Your agent drafts, you decide what gets posted.Honest boundaries: we do not monitor X, LinkedIn, or "the whole internet". Bounded sources, useful mentions.Launch offer: 14-day free trial plus free MCP setup help. I will personally help you create your first monitors and connect your agent.Tell me what your agent workflow needs. Ask me anything!