nirholas/community-health-monitoring

Community Health Monitoring

MCP-powered workflow for auditing follower quality, engagement health, and network efficiency. Produces a scored health report.

MCP Tools Used

| Tool | Purpose | |------|---------| | x_get_profile | Account-level stats | | x_get_followers | Follower list for quality audit | | x_get_following | Following list for reciprocity check | | x_get_non_followers | Identify non-reciprocal follows | | x_get_tweets | Engagement data for authenticity check | | x_detect_unfollowers | Track recent unfollower patterns |

Browser Scripts

Complement MCP analysis with browser-side tools:

| Goal | Script | |------|--------| | Audit follower quality | src/auditFollowers.js | | Detect unfollowers | src/detectUnfollowers.js | | Audience demographics | src/audienceDemographics.js | | Follow ratio analysis | src/followRatioManager.js | | Account health dashboard | src/accountHealthMonitor.js | | Shadowban check | src/shadowbanChecker.js |

Workflow

1. Profile baseline -- Call x_get_profile to get follower count, following count, and calculate follower-to-following ratio.
2. Audit follower quality -- Call x_get_followers with limit: 200. Classify each follower:
   • Active: Has bio, 50+ followers, posted in last 30 days
   • Low quality: No bio, <10 followers, or no recent activity
   • Suspect bot: Default avatar, username with many numbers, 0 tweets, follows 1000+
3. Check engagement authenticity -- Call x_get_tweets with limit: 30. For each tweet, compare engagement volume to follower count. Flag anomalies: likes/follower ratio > 10% (potential engagement pods) or < 0.1% (ghost followers).
4. Analyze unfollower patterns -- Call x_detect_unfollowers. Note churn rate and whether unfollowers correlate with specific content types or posting gaps.
5. Assess reciprocity -- Call x_get_non_followers. Calculate reciprocity rate: mutual_follows / total_following * 100. Identify high-value accounts not following back.
6. Calculate health score -- Weighted composite (0-100):
   • Follower quality: 30% (% active followers)
   • Engagement authenticity: 25% (normal engagement patterns)
   • Churn rate: 20% (low unfollower rate)
   • Reciprocity: 15% (healthy follower/following balance)
   • Growth trend: 10% (net positive follower change)
7. Generate report -- Compile into the template below with actionable recommendations.

Output Template

## Community Health Report: @{username}
Date: {date} | Health Score: {score}/100

### Score Breakdown
| Category | Score | Weight | Weighted |
|----------|-------|--------|----------|
| Follower Quality | {n}/100 | 30% | {n} |
| Engagement Authenticity | {n}/100 | 25% | {n} |
| Churn Rate | {n}/100 | 20% | {n} |
| Reciprocity | {n}/100 | 15% | {n} |
| Growth Trend | {n}/100 | 10% | {n} |

### Follower Audit
- Total: {count} | Active: {n}% | Low quality: {n}% | Suspect bots: {n}%

### Engagement Health
- Avg engagement rate: {rate}%
- Anomalous posts: {count} flagged

### Reciprocity
- Following: {count} | Follow back: {n}% | Non-followers: {count}

### Recommendations
1. {actionable recommendation}
2. {actionable recommendation}
3. {actionable recommendation}

Strategy Guide

Monthly health audit routine

1. Run full MCP workflow above for baseline report
2. Compare against previous month's scores
3. Action items: block flagged bots, unfollow non-reciprocals above threshold
4. Use src/accountHealthMonitor.js for quick between-audit checks

Score interpretation

| Score | Grade | Action | |-------|-------|--------| | 80-100 | Excellent | Maintain current strategy | | 60-79 | Good | Minor adjustments needed | | 40-59 | Fair | Review engagement strategy, clean follower list | | 20-39 | Poor | Major cleanup needed, block bots, reassess content | | 0-19 | Critical | Possible shadowban, mass bot followers, or inactive account |

Improving a low health score

1. Block suspect bot followers with src/blockBots.js
2. Unfollow non-reciprocals with src/unfollowback.js
3. Increase posting consistency to reduce churn
4. Engage authentically to improve engagement rate

Notes

• Health score is a heuristic -- use as directional guidance, not exact measurement
• Bot detection uses profile signals, not ML -- some false positives expected
• Run quarterly for trend tracking, monthly for active management