anysite-audience-analysis

name

anysite-audience-analysis

description

Analyze audience demographics, engagement patterns, and follower behavior across Instagram, YouTube, and LinkedIn using anysite MCP server. Understand who engages with content, track audience growth, analyze follower quality, identify engagement patterns, and profile audience characteristics. Supports Instagram audience analysis, YouTube subscriber research, and LinkedIn connection profiling. Use when users need to understand target audiences, validate influencer audiences, analyze follower demographics, track engagement patterns, or optimize content for specific audience segments.

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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