anysite Trend Analysis

Discover emerging trends and track viral content across social platforms using anysite MCP. Identify what's gaining momentum before it peaks.

Overview

• Detect emerging trends across multiple platforms
• Track viral content and identify breakout topics
• Monitor hashtag performance and trending keywords
• Analyze topic momentum and growth patterns
• Identify market shifts through social listening

Coverage: 75% - Good for Twitter, Reddit, YouTube, LinkedIn, Instagram

Supported Platforms

• Twitter/X: Trending topics, viral tweets, hashtag tracking
• Reddit: Trending posts, subreddit activity, upvote velocity
• YouTube: Trending videos, search trends, rising channels
• LinkedIn: Professional trends, industry discussions
• Instagram: Trending hashtags, viral content

v2 Tool Interface

All data fetching uses the universal execute() meta-tool. Always call discover(source, category) first if you need to verify endpoint names or available parameters.

Core tools:

• execute(source, category, endpoint, params) - Fetch data. Returns first page + cache_key.
• get_page(cache_key, offset, limit) - Load more results from a previous execute.
• query_cache(cache_key, conditions, sort_by, aggregate, group_by) - Filter, sort, or aggregate cached data without new API calls.
• export_data(cache_key, format) - Export full dataset as CSV, JSON, or JSONL.

Error handling: If execute() returns an error with llm_hint, follow the hint to fix the request (e.g., correcting a parameter name or adjusting the query).

Quick Start

Step 1: Search for Trending Content

By platform:

• Twitter: execute("twitter", "search", "search_tweets", {"query": "<topic>", "count": 100}) sorted by engagement
• Reddit: execute("reddit", "search", "search", {"query": "<topic>"}) sorted by upvotes
• YouTube: execute("youtube", "search", "search_videos", {"query": "<topic>", "count": 50}) by recent
• LinkedIn: execute("linkedin", "post", "search_posts", {"keywords": "<topic>"}) by engagement
• Instagram: execute("instagram", "search", "search_users", {"query": "<topic>"}) for hashtag/topic discovery

Step 2: Analyze Momentum

Use query_cache() to filter and sort cached results:

query_cache(cache_key, sort_by="engagement_desc", conditions=[{"field": "date", "op": ">", "value": "2024-01-01"}])

Check indicators:

• Engagement velocity (growth rate)
• Cross-platform presence
• Comment volume and sentiment
• Share/retweet patterns

Step 3: Track Over Time

Monitor changes:

• Daily engagement growth
• New platform adoption
• Mainstream vs. niche spread
• Peak timing prediction

Step 4: Report Insights

Use export_data(cache_key, "csv") to generate downloadable reports.

Deliver:

• Trending topics list
• Momentum indicators
• Strategic recommendations
• Early warnings or opportunities

Common Workflows

Workflow 1: Multi-Platform Trend Detection

Scenario: Identify what's trending in tech/AI space

Steps:

1. Search Across Platforms

# Twitter
execute("twitter", "search", "search_tweets", {"query": "AI OR artificial intelligence", "count": 100})
→ Filter for: Posted within 24-48h, high engagement
→ Save cache_key as twitter_cache

# Reddit
execute("reddit", "search", "search", {"query": "artificial intelligence"})
→ Filter: r/technology, r/MachineLearning, r/singularity
→ Save cache_key as reddit_cache

# YouTube
execute("youtube", "search", "search_videos", {"query": "AI news", "count": 50})
→ Filter: Published this week, views >10k
→ Save cache_key as youtube_cache

# LinkedIn
execute("linkedin", "post", "search_posts", {"keywords": "artificial intelligence"})
→ Filter: High engagement, recent
→ Save cache_key as linkedin_cache

2. Use query_cache to Filter Results

# Filter Twitter for high-engagement posts
query_cache(twitter_cache, sort_by="engagement_desc", conditions=[{"field": "likes", "op": ">", "value": 100}])

# Filter Reddit for specific subreddits
query_cache(reddit_cache, conditions=[{"field": "subreddit", "op": "contains", "value": "technology"}])

# Aggregate YouTube view counts
query_cache(youtube_cache, aggregate={"field": "views", "op": "avg"})

3. Load More Results if Needed

# If execute() returned next_offset, paginate
get_page(twitter_cache, offset=10, limit=50)
get_page(reddit_cache, offset=10, limit=50)

4. Extract Common Themes

Analyze content for recurring:
- Keywords and phrases
- Company/product mentions
- Events or announcements
- Questions or concerns

5. Calculate Trend Score

For each theme:
- Platform count (how many platforms)
- Total engagement
- Growth velocity
- Sentiment distribution

6. Identify Breakout Trends

Trends with:
- Presence on 3+ platforms
- Engagement growing >50% daily
- Positive or controversial sentiment
- Coverage by influencers/media

Expected Output:

• Top 5-10 trending themes
• Platform-by-platform breakdown
• Momentum indicators
• Strategic implications

Workflow 2: Hashtag Performance Tracking

Scenario: Monitor hashtag growth and adoption

Steps:

1. Search by Hashtag

# Instagram - discover users/content around the hashtag
execute("instagram", "search", "search_users", {"query": "sustainability"})
→ Save cache_key as ig_cache

# Twitter
execute("twitter", "search", "search_tweets", {"query": "#sustainability", "count": 100})
→ Track tweet volume over time
→ Save cache_key as tw_cache

# LinkedIn
execute("linkedin", "post", "search_posts", {"keywords": "sustainability"})
→ Check professional adoption
→ Save cache_key as li_cache

2. Calculate Velocity with query_cache

# Sort by recency and engagement
query_cache(tw_cache, sort_by="date_desc")
query_cache(ig_cache, sort_by="followers_desc")

Hashtag velocity:
- Posts in last 24h vs. previous 24h
- Engagement rate change
- New accounts using hashtag
- Geographic spread

3. Analyze Content Evolution

Compare early vs. recent posts:
- Topic shifts
- Audience changes
- Influencer involvement
- Commercial adoption

4. Export Results

export_data(tw_cache, "csv")
export_data(ig_cache, "json")
→ Share downloadable reports

5. Predict Peak

Based on growth curve:
- Early stage (accelerating)
- Peak stage (plateauing)
- Decline stage (slowing)

Expected Output:

• Hashtag performance report
• Growth trajectory
• Peak timing estimate
• Strategic recommendations

Workflow 3: Reddit Trend Mining

Scenario: Find emerging discussions in specific communities

Steps:

1. Search Target Subreddits

execute("reddit", "posts", "get", {"subreddit": "technology"})
→ Get top posts from last week
→ Save cache_key as reddit_tech_cache

2. Analyze Post Momentum

# Sort cached posts by engagement
query_cache(reddit_tech_cache, sort_by="upvotes_desc")

# Aggregate engagement metrics
query_cache(reddit_tech_cache, aggregate={"field": "upvotes", "op": "avg"})

For each high-momentum post:
  execute("reddit", "search", "search", {"query": "<post topic>"})
  → Deeper analysis

Calculate:
- Upvotes per hour
- Comment velocity
- Award count
- Controversial score

3. Extract Discussion Themes

From high-momentum posts:
- What problems are discussed?
- What solutions are proposed?
- What companies/products mentioned?
- What sentiment (positive, negative, concerned)?

4. Track Cross-Pollination

Check if trending Reddit topics appear on:
- Twitter: execute("twitter", "search", "search_tweets", {"query": "<topic>"})
- LinkedIn: execute("linkedin", "post", "search_posts", {"keywords": "<topic>"})
- YouTube: execute("youtube", "search", "search_videos", {"query": "<topic>"})

Expected Output:

• Top Reddit trends
• Community sentiment
• Mainstream potential
• Early mover opportunities

MCP Tools Reference (v2)

Twitter/X

• execute("twitter", "search", "search_tweets", {"query": ..., "count": N}) - Find tweets, filter by engagement
• execute("twitter", "user", "get", {"username": ...}) - Check influencer adoption

Reddit

• execute("reddit", "search", "search", {"query": ...,}) - Find discussions
• execute("reddit", "posts", "get", {"subreddit": ...}) - Get subreddit posts and momentum
• execute("reddit", "user", "get", {"username": ...}) - Get user details

YouTube

• execute("youtube", "search", "search_videos", {"query": ..., "count": N}) - Find trending videos
• execute("youtube", "video", "video", {"video": ...}) - Track view velocity
• execute("youtube", "video", "video_comments", {"video": ..., "count": N}) - Gauge interest

LinkedIn

• execute("linkedin", "post", "search_posts", {"keywords": ...}) - Professional trends
• execute("linkedin", "company", "get", {"company": ...}) - Company details

Instagram

• execute("instagram", "search", "search_users", {"query": ...}) - Discover users/hashtags
• execute("instagram", "post", "post", {"post": ...}) - Engagement metrics

Pagination & Analysis

• get_page(cache_key, offset, limit) - Load additional results from any execute() call
• query_cache(cache_key, conditions, sort_by, aggregate, group_by) - Filter, sort, aggregate cached data
• export_data(cache_key, "csv"|"json"|"jsonl") - Export datasets for reporting

Trend Identification Framework

Trend Stages:

1. Emergence (0-20% awareness)

   • Niche communities discussing
   • Low but accelerating engagement
   • Early adopters experimenting
   • Action: Monitor closely, prepare strategy

2. Growth (20-50% awareness)

   • Crossing into mainstream platforms
   • Rapid engagement growth
   • Influencer adoption
   • Action: Create content, engage actively

3. Peak (50-80% awareness)

   • Maximum visibility
   • Slowing growth rate
   • Saturation approaching
   • Action: Maximize presence before decline

4. Decline (80-100% awareness)

   • Engagement decreasing
   • Moving to "background noise"
   • New trends emerging
   • Action: Shift focus to next trend

Momentum Indicators:

• Volume: Mentions per day
• Velocity: Growth rate (% change)
• Reach: Unique accounts discussing
• Spread: Number of platforms
• Sentiment: Positive/negative ratio
• Influence: Key accounts involved

Output Formats

Chat Summary:

• Top 5 trends with momentum scores
• Platform breakdown
• Strategic recommendations

CSV Export (via export_data(cache_key, "csv")):

• Trend name, platforms, volume
• Growth rate, sentiment
• Key influencers mentioning

JSON Export (via export_data(cache_key, "json")):

• Complete trend data
• Time-series metrics
• Cross-platform correlations

Reference Documentation

• SOCIAL_MONITORING.md - Social listening techniques, monitoring strategies, and trend prediction methods

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Ready to discover trends? Ask Claude to help you identify emerging topics, track viral content, or monitor market shifts across social platforms!