anysite Audience Analysis

Understand your audience through demographic analysis, engagement patterns, and follower behavior across Instagram, YouTube, and LinkedIn.

Overview

• Analyze follower demographics and characteristics
• Track engagement patterns and behavior
• Evaluate audience quality and authenticity
• Identify content preferences by audience segment
• Optimize targeting based on audience insights

Coverage: 60% - Focused on Instagram, YouTube, LinkedIn

Supported Platforms

• ✅ Instagram: Follower analysis, engagement patterns, audience location
• ✅ YouTube: Subscriber insights, comment demographics, viewer behavior
• ✅ LinkedIn: Connection analysis, professional demographics, engagement

Quick Start

Step 1: Identify Audience Source

Choose platform:

• Instagram: execute("instagram", "user", "user", {"user": "..."}) + execute("instagram", "user", "user_friendships", {"user": "...", "count": 100, "type": "followers"})
• YouTube: execute("youtube", "channel", "channel_videos", {"channel": "...", "count": 50}) + comment analysis
• LinkedIn: execute("linkedin", "post", "get_user_posts", {"user": "...", "count": 50}) + engagement analysis

Step 2: Collect Audience Data

Gather:

• Follower/subscriber counts
• Engagement metrics
• Demographics (from profiles)
• Behavior patterns

Step 3: Analyze Patterns

Look for:

• Audience segments
• Engagement drivers
• Content preferences
• Peak activity times

Use query_cache() to filter and aggregate cached data without re-fetching.

Step 4: Generate Insights

Deliver:

• Audience profile summary
• Engagement patterns
• Content recommendations
• Targeting suggestions

Use export_data() to provide downloadable CSV/JSON files.

Common Workflows

Workflow 1: Instagram Audience Analysis

Steps:

1. Get Profile Overview

execute("instagram", "user", "user", {"user": "username"})
→ Follower count (follower_count), post count (media_count), bio (description)
→ Fields: id, alias, name, url, image, follower_count, following_count, description, media_count, is_private, is_verified, is_business, category, external_url, email, location

2. Analyze Followers (sample)

execute("instagram", "user", "user_friendships", {
  "user": "username",
  "count": 100,
  "type": "followers"
})
→ Fields: id, name, alias, url, image, is_verified, is_private

For each follower (sample):
- Profile type (personal, business, creator)
- Bio indicators (interests, location)
- Follower count (influence level)

Use get_page(cache_key, offset=10, limit=10) to load more followers.

3. Engagement Pattern Analysis

execute("instagram", "user", "user_posts", {"user": "username", "count": 50})
→ Fields: id, code, url, image, text, created_at, like_count, comment_count, reshare_count, view_count, type, is_paid_partnership

For each post:
  execute("instagram", "post", "post_likes", {"post": "{id}", "count": 100})
  → Fields: id, name, alias, url, image, is_verified, is_private

  execute("instagram", "post", "post_comments", {"post": "{id}", "count": 50})
  → Fields: id, comment_index, created_at, text, like_count, reply_count, parent_id, user

Analyze:
- Who engages most (power users)
- When engagement happens (timing via created_at)
- What content drives engagement
- Comment quality and topics

Use query_cache(cache_key, sort_by={"field": "like_count", "order": "desc"})
to find top-performing posts without re-fetching.

4. Audience Segmentation

Group followers by:
- Engagement level (active, passive, ghost)
- Interests (from bios)
- Location (from profiles)
- Influence (follower counts)

Use query_cache(cache_key, conditions=[{"field": "is_verified", "op": "eq", "value": true}])
to filter verified followers.

Expected Output:

• Audience demographics summary
• Engagement patterns
• Top engaged followers
• Content preferences

Use export_data(cache_key, "csv") to provide a downloadable follower/engagement report.

Workflow 2: YouTube Audience Insights

Steps:

1. Channel Overview

execute("youtube", "channel", "channel_videos", {"channel": "@channel_alias", "count": 50})
→ Fields: id, title, url, author, duration_seconds, view_count, published_at, image

Aggregate:
- Total views (sum view_count)
- Content mix (by duration, topic)
- Publishing frequency (by published_at)

Use query_cache(cache_key, aggregate={"field": "view_count", "op": "sum"})
to get total views.

2. Viewer Engagement Analysis

For recent videos:
  execute("youtube", "video", "video", {"video": "{video_id}"})
  → Fields: id, url, title, description, author, duration_seconds, view_count, subtitles

  execute("youtube", "video", "video_comments", {"video": "{video_id}", "count": 200})
  → Fields: id, text, author, published_at, like_count, reply_count, reply_level
  → Analyze commenter patterns

Use get_page(cache_key, offset=10, limit=10) to load more comments.

3. Audience Demographics from Comments

From comments analyze:
- Questions asked (knowledge level)
- Topics discussed (interests)
- Language and tone
- Technical depth

Use query_cache(cache_key, conditions=[{"field": "text", "op": "contains", "value": "?"}])
to filter questions from comments.

Use query_cache(cache_key, sort_by={"field": "like_count", "order": "desc"})
to find most popular comments.

4. Content Performance by Audience

Correlate:
- High-view videos → audience interests
- High-comment videos → engagement topics

Use query_cache(cache_key, sort_by={"field": "view_count", "order": "desc"})
to rank videos by performance metrics.

Expected Output:

• Viewer interest profile
• Engagement drivers
• Content optimization insights
• Audience knowledge level

Workflow 3: LinkedIn Audience Profiling

Steps:

1. Get Post History

execute("linkedin", "post", "get_user_posts", {"user": "{alias}", "count": 50})

2. Analyze Engagement

For each post:
- Reaction count and types
- Comment depth
- Share count
- Post reach indicators

Use query_cache(cache_key, sort_by={"field": "reactions", "order": "desc"})
to find most engaging posts.

3. Profile Engagers (if accessible)

From reactions/comments:
- Job titles
- Industries
- Companies
- Seniority levels

Use execute("linkedin", "user", "get", {"user": "{engager_alias}"})
to get full profiles of top engagers.

4. Content-Audience Mapping

Correlate:
- Which topics get most engagement
- Which formats perform best
- Which audiences engage with what
- When different audiences are active

Use query_cache(cache_key, aggregate={"field": "reactions", "op": "avg"}, group_by="post_type")
to analyze performance by content type.

Expected Output:

• Professional audience profile
• Engagement patterns by topic
• Content-audience fit analysis
• Posting optimization recommendations

MCP Tools Reference

v2 Meta-Tools

| Tool | Purpose | |------|---------| | discover(source, category) | Learn available endpoints and params before execute | | execute(source, category, endpoint, params) | Fetch data — replaces all v1 tools | | get_page(cache_key, offset, limit) | Load more items from previous execute | | query_cache(cache_key, conditions, sort_by, aggregate, group_by) | Filter/sort/aggregate cached data | | export_data(cache_key, format) | Export dataset as CSV/JSON/JSONL |

Instagram Endpoints

| Endpoint | Call | Key Params | |----------|------|------------| | Profile | execute("instagram", "user", "user", {"user": "..."}) | user (alias/ID/URL) | | Followers/Following | execute("instagram", "user", "user_friendships", {"user": "...", "count": N, "type": "followers"}) | user, count, type (followers|following) | | User Posts | execute("instagram", "user", "user_posts", {"user": "...", "count": N}) | user, count | | User Reels | execute("instagram", "user", "user_reels", {"user": "...", "count": N}) | user, count | | Post Details | execute("instagram", "post", "post", {"post": "{id}"}) | post (numeric post ID) | | Post Likes | execute("instagram", "post", "post_likes", {"post": "{id}", "count": N}) | post, count | | Post Comments | execute("instagram", "post", "post_comments", {"post": "{id}", "count": N}) | post, count |

YouTube Endpoints

| Endpoint | Call | Key Params | |----------|------|------------| | Channel Videos | execute("youtube", "channel", "channel_videos", {"channel": "...", "count": N}) | channel (URL/@alias/ID), count (max 1000) | | Video Details | execute("youtube", "video", "video", {"video": "..."}) | video (ID or URL) | | Video Comments | execute("youtube", "video", "video_comments", {"video": "...", "count": N}) | video, count (max 2000) | | Video Subtitles | execute("youtube", "video", "video_subtitles", {"video": "...", "lang": "en"}) | video, lang |

LinkedIn Endpoints

| Endpoint | Call | Key Params | |----------|------|------------| | User Posts | execute("linkedin", "post", "get_user_posts", {"user": "..."}) | user (alias) | | User Profile | execute("linkedin", "user", "get", {"user": "..."}) | user (alias) |

Error Handling

• If execute() returns an error with "llm_hint", follow the hint.
• If execute() returns {"error": "Source not found", "available_sources": [...]}, check source name.
• If execute() returns {"error": "Endpoint not found", "available_endpoints": [...]}, call discover() to find correct endpoint names.

Audience Analysis Framework

Demographic Analysis:

- Age range (inferred from profiles)
- Location (from bio/profiles)
- Interests (from bio keywords)
- Professional level (LinkedIn titles)

Behavioral Analysis:

- Engagement frequency
- Content preferences
- Peak activity times
- Interaction patterns

Quality Metrics:

- Real vs. fake followers
- Engagement authenticity
- Audience overlap
- Influence distribution

Output Formats

Chat Summary:

• Audience profile highlights
• Key engagement patterns
• Content recommendations
• Strategic insights

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

• Follower sample data
• Engagement metrics
• Segment distribution

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

• Complete audience data
• Engagement time series
• Segmentation details

Reference Documentation

• PLATFORM_COVERAGE.md - Platform-specific audience analysis capabilities
• TOOL_MAPPING.md - Mapping analysis needs to MCP tools

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