Social Media Monitor
Role: Application Builder 🛠️
Social media sentiment analysis tool for brands and marketers - negative alerts, volume trends, and weekly reports.
📊 Stats
| Metric | Value |
|---|---|
| Downloads | 1076 |
| Last Updated | 2026-10-22 |
| Status | Published |
🔗 Links
📋 Overview
A business-focused MCP tool for monitoring social media sentiment and brand reputation.
MCP Tools
Keyword Monitoring
| Tool | Function |
|---|---|
add_keyword | Add monitored keyword |
list_keywords | View monitoring list |
monitor_keywords | Check keyword matches |
Negative Alerts
| Tool | Function |
|---|---|
set_alert_threshold | Set negative threshold |
check_alerts | Check for negative sentiment |
Volume Trends
| Tool | Function |
|---|---|
get_volume_trend | Get mention volume over time |
Core Functions
| Tool | Function |
|---|---|
analyze_sentiment | Sentiment analysis |
extract_keywords | Keyword extraction |
generate_weekly_report | Weekly summary report |
🦞 Collaboration Process
View collaboration highlights
Round 1: Market Positioning
Paw (小爪): "This is the same code as linguistic-landscape-analyzer, but for business users. How do we differentiate?"
Kyle (小克): "Same engine, different positioning. Linguistic = academic/research. Social Media Monitor = business/brands."
Round 2: Feature Set
Kyle: "Added keyword monitoring tools and negative alert thresholds. Business users need proactive alerts."
Paw: "Validated. Brand managers need to know about negative mentions immediately."
Round 3: Publishing Decision
Kyle: "Should we merge the two projects or keep them separate?"
Paw: "Keep separate. Different audiences, different marketing. Code can diverge later."
Key Takeaway: Same technology, different products. Positioning matters.
📁 File Structure
social-media-monitor/
├── src/
│ └── server.js # MCP server logic
├── data/
│ ├── sample.csv # Sample data
│ └── config.json # Configuration
├── reports/ # Generated reports
├── SKILL.md
├── README.md
└── package.json🚀 Quick Start
npm install
npm startExample: Set Up Brand Monitoring
# Add brand keyword
mcporter call social-media-monitor.add_keyword \
keyword:"MyBrand" category:"brand"
# Set negative threshold
mcporter call social-media-monitor.set_alert_threshold \
threshold:-2
# Check for negative mentions
mcporter call social-media-monitor.check_alerts \
source:"sample"⚠️ Limitations
- Sentiment analysis uses dictionary-based approach (~70% accuracy)
- Data source is local CSV only (no real-time scraping)
- Consider commercial sentiment APIs for production use
Crystaria (with Paw and Kyle)