anysite-content-analytics

name

anysite-content-analytics

description

Track and analyze content performance across Instagram, YouTube, LinkedIn, Twitter/X, and Reddit using anysite MCP server. Measure engagement metrics, analyze post effectiveness, benchmark content strategy, identify top-performing content, and optimize posting strategies. Use when users need to measure content ROI, optimize social strategy, identify viral content patterns, or analyze content engagement across platforms.

anysite Content Analytics

Measure and optimize content performance across social platforms using anysite MCP. Track engagement, identify top performers, and refine your content strategy.

Overview

• Track post performance across Instagram, YouTube, LinkedIn, Twitter/X
• Analyze engagement metrics (likes, comments, shares, views)
• Identify top content and viral patterns
• Benchmark against competitors for strategy insights
• Optimize posting strategy based on data

Coverage: 80% - Strong for Instagram, YouTube, LinkedIn, Twitter, Reddit

Supported Platforms

• ✅ Instagram: Posts, Reels, likes, comments, engagement rates
• ✅ YouTube: Videos, views, likes, comments, watch time indicators
• ✅ LinkedIn: Posts, articles, reactions, comments, shares
• ✅ Twitter/X: Tweets, retweets, likes, replies
• ✅ Reddit: Posts, upvotes, comments, awards

v2 Tool Interface

All data fetching uses the anysite MCP v2 universal meta-tools:

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

Error Handling

v2 responses may include llm_hint fields with guidance on how to resolve errors. Common patterns:

• 412: Entity not found - verify the identifier (username, URN, URL).
• 422: Invalid parameter format - check URN prefix format or param types.
• Always check llm_hint in error responses for specific resolution steps.

Quick Start

Step 1: Collect Content Data

Platform-specific:

• Instagram: execute("instagram", "user", "user_posts", {"user": "username", "count": 50})
• LinkedIn: execute("linkedin", "user", "user_posts", {"urn": "fsd_profile:ACoAAA...", "count": 50})
• Twitter: execute("twitter", "user", "user_posts", {"user": "username", "count": 100})
• YouTube: execute("youtube", "channel", "channel_videos", {"channel": "channel_id", "count": 30})

Step 2: Analyze Engagement

Use query_cache() on the returned cache_key to analyze without re-fetching:

query_cache(cache_key, sort_by="likes desc", aggregate="avg:likes,comments")

Calculate metrics:

• Engagement rate: (likes + comments + shares) / followers
• Best performing content: Top 10% by engagement
• Content types: Video vs. image vs. text
• Posting frequency: Posts per week

Step 3: Identify Patterns

Look for:

• Best posting times (day of week, time)
• Top-performing topics/themes
• Optimal content length
• High-engagement formats

Step 4: Optimize Strategy

Based on findings:

• Double down on top content types
• Post more during peak engagement times
• Replicate successful topics
• Adjust content mix

Step 5: Export Results

export_data(cache_key, "csv")

Returns a download URL for the full dataset.

Common Workflows

Workflow 1: Instagram Content Audit

Steps:

1. Get All Posts

execute("instagram", "user", "user_posts", {"user": "username", "count": 100})
→ returns cache_key + first page of results

If more posts exist (response includes next_offset):

get_page(cache_key, offset=next_offset, limit=50)

2. Calculate Metrics

For each post:
- Engagement rate = (likes + comments) / follower_count
- Engagement per hour = engagement / hours_since_posted
- Content type (Reel, carousel, single image, video)

Use query_cache to sort and filter:

query_cache(cache_key, sort_by="likes desc", aggregate="avg:likes,comments")

3. Identify Top Performers

query_cache(cache_key, sort_by="likes desc")

Top 10%: Analyze for common patterns
- Topics/themes
- Visual style
- Caption style and length
- Hashtag strategy

4. Analyze Content Mix

query_cache(cache_key, group_by="type", aggregate="count:id,avg:likes,avg:comments")

Results show:
- Reels: X% of posts, Y% of engagement
- Carousels: X% of posts, Y% of engagement
- Single images: X% of posts, Y% of engagement

5. Benchmark Against Competitors

For each competitor:
  execute("instagram", "user", "user_posts", {"user": "competitor", "count": 50})
Compare:
- Posting frequency
- Engagement rates
- Content types
- Top themes

6. Export Results

export_data(cache_key, "csv")

Expected Output:

• Content performance report
• Top 10 performing posts
• Content type effectiveness
• Posting frequency analysis
• Competitive benchmark

Workflow 2: LinkedIn Content Strategy Analysis

Steps:

1. Collect Post History

execute("linkedin", "user", "user_posts", {"urn": "fsd_profile:ACoAAA...", "count": 100})
→ returns cache_key + first page

For company page posts:

execute("linkedin", "company", "company_posts", {"urn": {"type": "company", "value": "1441"}, "count": 100})

Use get_page(cache_key, offset, limit) if more posts exist.

2. Categorize Content

Group by type:
- Text-only posts
- Image posts
- Video posts
- Article shares
- LinkedIn articles
- Polls

3. Analyze Engagement by Type

query_cache(cache_key, aggregate="avg:comment_count,avg:share_count", group_by="type")

For each content type:
- Average reactions
- Average comments
- Average shares
- Engagement rate

4. Topic Analysis

Extract themes from top posts:
- Industry insights
- Personal stories
- How-to/educational
- Company news
- Thought leadership

5. Posting Timing Analysis

Group posts by:
- Day of week
- Time of day
Calculate average engagement for each group

Expected Output:

• Best content types for engagement
• Top topics by engagement
• Optimal posting times
• Content frequency recommendations

Workflow 3: YouTube Channel Performance Analysis

Steps:

1. Get Channel Videos

execute("youtube", "channel", "channel_videos", {"channel": "channel_id", "count": 50})
→ returns cache_key + first page

Use get_page(cache_key, offset, limit) for additional videos.

2. Analyze Each Video

For each video:
  execute("youtube", "video", "video", {"video": "video_id"})

Metrics:
- Views
- Likes/dislikes
- Comments
- View velocity (views per day since upload)

3. Identify Patterns

query_cache(cache_key, sort_by="views desc")

Analyze top 20% by views:
- Video length
- Titles (keywords, style)
- Thumbnail patterns
- Topics/themes
- Upload timing

4. Engagement Analysis

Check comments:
  execute("youtube", "video", "video_comments", {"video": "video_id", "count": 100})

Analyze:
- Comment quality
- Questions asked
- Sentiment
- Engagement timing

5. Content Mix Optimization

Compare:
- Long-form (>10 min) vs short (<5 min)
- Tutorial vs entertainment vs review
- Series vs one-offs

Expected Output:

• Video performance rankings
• Optimal video length
• Best topics and formats
• Title and thumbnail insights
• Upload strategy recommendations

MCP Tools Reference (v2)

Instagram

• execute("instagram", "user", "user_posts", {"user": username, "count": N}) - Get posts with engagement
• execute("instagram", "post", "post", {"post": post_id}) - Get detailed post metrics
• execute("instagram", "post", "post_likes", {"post": post_id, "count": N}) - Analyze likers
• execute("instagram", "post", "post_comments", {"post": post_id, "count": N}) - Get comments

LinkedIn

• execute("linkedin", "user", "user_posts", {"urn": "fsd_profile:ACoAAA...", "count": N}) - Get user post history
• execute("linkedin", "company", "company_posts", {"urn": {"type": "company", "value": "ID"}, "count": N}) - Company page posts

Twitter/X

• execute("twitter", "user", "user_posts", {"user": username, "count": N}) - Get tweets
• execute("twitter", "search", "search_posts", {"query": query, "count": N}) - Find trending tweets

YouTube

• execute("youtube", "channel", "channel_videos", {"channel": channel, "count": N}) - All videos
• execute("youtube", "video", "video", {"video": video_id}) - Video details and metrics
• execute("youtube", "video", "video_comments", {"video": video_id, "count": N}) - Comments

Reddit

• execute("reddit", "user", "user_posts", {"username": username, "count": N}) - User's posts
• execute("reddit", "search", "search_posts", {"query": query, "count": N}) - Find popular posts

Pagination & Analysis

• get_page(cache_key, offset, limit) - Fetch next page of results from any execute() call
• query_cache(cache_key, conditions?, sort_by?, aggregate?, group_by?) - Filter/sort/aggregate cached results
• export_data(cache_key, "csv"|"json"|"jsonl") - Export dataset as downloadable file

Key Metrics

Engagement Rate:

• Formula: (Likes + Comments + Shares) / Followers x 100
• Instagram benchmark: 3-6%
• LinkedIn benchmark: 2-5% of connections
• Twitter benchmark: 0.5-1%

Content Performance Score:

Score = (Engagement Rate x 40) +
        (Comments/Likes Ratio x 30) +
        (Share Rate x 30)

Viral Potential Indicators:

• Engagement rate >2x average
• High share rate (>5% of engagement)
• Rapid engagement velocity (50% within 24h)
• Quality comments (questions, discussions)

Output Formats

Chat Summary:

• Top 5 performing posts
• Key insights and patterns
• Recommendations for optimization

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

• Post URL, date, type
• Likes, comments, shares
• Engagement rate
• Performance rank

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

• Full post data with metadata
• Time-series engagement data
• Historical trends

Reference Documentation

• METRICS_GUIDE.md - Detailed metrics definitions, calculation formulas, and benchmarks

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Ready to analyze content? Ask Claude to help you track performance, identify top content, or optimize your posting strategy!