anysite Influencer Discovery

Find and analyze influencers across social platforms using anysite MCP. Discover content creators, evaluate their reach and engagement, and identify partnership opportunities.

Overview

• Discover influencers across Instagram, Twitter, LinkedIn, YouTube
• Analyze engagement and audience quality
• Track activity and content patterns
• Evaluate partnership fit based on niche and metrics
• Build influencer lists with contact information

Coverage: 85% - Excellent for Instagram, Twitter, LinkedIn, YouTube influencers.

v2 Tool Interface

All data fetching uses the anysite v2 meta-tools:

• execute(source, category, endpoint, params) - Fetch data. 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, or 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: Check responses for llm_hint fields that provide actionable guidance on failures (e.g., alias not found, URN required).

Supported Platforms

• ✅ Instagram: Profile stats, posts, followers, engagement, Reels
• ✅ Twitter/X: User search, followers, tweets, engagement
• ✅ LinkedIn: B2B influencers, thought leaders, professional content
• ✅ YouTube: Channel search, subscribers, views, video performance
• ✅ Reddit: Community influencers, karma, post quality

Quick Start

Step 1: Search for Influencers

By platform:

• Instagram: execute("instagram", "search", "search_posts", {"query": "niche keywords", "count": 50}) with niche keywords + hashtags
• Twitter: execute("twitter", "search", "search_users", {"query": "niche keywords", "count": 50}) with niche keywords
• LinkedIn: execute("linkedin", "search", "search_users", {"keywords": "industry thought leader", "count": 50}) with industry + "thought leader"
• YouTube: execute("youtube", "search", "search_videos", {"query": "niche", "count": 50}) with niche, then analyze channels

Step 2: Analyze Profiles

Get detailed metrics:

• Instagram: execute("instagram", "user", "user", {"user": "username"}) -> followers, posts, engagement rate
• Twitter: execute("twitter", "user", "get", {"username": "handle"}) -> followers, tweet frequency
• YouTube: execute("youtube", "channel", "channel_videos", {"channel": "...", "count": 30}) -> subscribers, views, growth
• LinkedIn: execute("linkedin", "user", "user", {"user": "alias"}) -> connections, post engagement

Step 3: Evaluate Engagement

Check engagement quality:

• Post likes, comments, shares
• Engagement rate (engagement / followers)
• Audience authenticity (comment quality)
• Content consistency (posts per week)

Use query_cache(cache_key, sort_by=[{"field": "like_count", "order": "desc"}]) to rank posts by engagement without re-fetching.

Step 4: Build Influencer List

Export with export_data(cache_key, "csv"):

• Name, handle, platform
• Follower count, engagement rate
• Niche/topics, content type
• Contact info (if available)
• Partnership fit score

Common Workflows

Workflow 1: Instagram Influencer Discovery

Scenario: Find Instagram influencers in sustainable fashion (10k-100k followers)

Steps:

1. Search by Hashtag/Keywords

execute("instagram", "search", "search_posts", {
  "query": "sustainable fashion OR eco friendly fashion",
  "count": 100
})
-> Extract unique user handles from results
-> Use get_page(cache_key, offset, 50) if next_offset returned for more results

2. Analyze Each Creator

For each unique handle:
  execute("instagram", "user", "user", {"user": "username"})
  -> Follower count, bio, profile type

Filter for:
- 10k-100k followers
- Business/Creator account
- Bio mentioning sustainability

3. Evaluate Content

For qualified creators:
  execute("instagram", "user", "user_posts", {"user": "username", "count": 30})

Analyze:
- Post frequency (consistency)
- Engagement rate per post
- Content quality and style
- Brand partnerships visible

Use query_cache(cache_key, sort_by=[{"field": "like_count", "order": "desc"}]) to find top posts
Use query_cache(cache_key, aggregate=[{"field": "like_count", "function": "avg"}]) for average engagement

4. Check Audience Quality

execute("instagram", "post", "post_likes", {"post": "post_id", "count": 100})
execute("instagram", "post", "post_comments", {"post": "post_id", "count": 50})

Look for:
- Real comments (not just emojis)
- Engaged community (questions, discussions)
- Geographic relevance

5. Get Contact Information

From Instagram bio:
- Email addresses
- Website links

If LinkedIn mentioned:
  execute("linkedin", "search", "search_users", {"keywords": "first_name last_name"})
  execute("linkedin", "user", "user", {"user": "alias_from_search"})

Expected Output:

• 20-40 qualified influencers
• Engagement metrics for each
• Contact information for 60-70%
• Partnership fit scores

Use export_data(cache_key, "csv") to generate a downloadable influencer list.

Workflow 2: LinkedIn Thought Leader Identification

Scenario: Find B2B thought leaders in SaaS/sales

Steps:

1. Search for Active Posters

execute("linkedin", "search", "search_users", {
  "keywords": "SaaS sales thought leader",
  "title": "VP Sales OR Head of Sales OR Chief Revenue Officer",
  "count": 100
})

2. Analyze Post Activity

For each candidate:
  execute("linkedin", "post", "get_user_posts", {"user": "urn", "count": 50})

Filter for:
- Posts 2-3x per week minimum
- High engagement (100+ reactions)
- Original content (not just shares)

Use query_cache(cache_key, conditions=[{"field": "comment_count", "operator": ">", "value": 10}])
to filter for high-engagement posts

3. Evaluate Influence

Check post engagement:
- Average reactions per post
- Comment quality and quantity
- Share count
- Follower growth signals

Use query_cache(cache_key, aggregate=[
  {"field": "comment_count", "function": "avg"},
  {"field": "share_count", "function": "avg"}
]) for average metrics

4. Assess Content Quality

Review posts for:
- Expertise demonstration
- Original insights
- Engagement with comments
- Consistency of messaging

Expected Output:

• 15-25 active thought leaders
• Content themes and topics
• Engagement metrics
• Partnership opportunities (guest posts, quotes, etc.)

Use export_data(cache_key, "csv") to export the thought leader list.

Workflow 3: YouTube Creator Research

Scenario: Find YouTube creators in tech reviews

Steps:

1. Search for Niche Content

execute("youtube", "search", "search_videos", {
  "query": "tech review 2026",
  "count": 100
})
-> Extract unique channel names
-> Use get_page(cache_key, offset, 50) if more results needed

2. Analyze Channels

For each channel:
  execute("youtube", "channel", "channel_videos", {"channel": "channel_id", "count": 30})

Check:
- Subscriber count
- Upload frequency
- Average views per video
- Video length (long-form vs shorts)

Use query_cache(cache_key, aggregate=[{"field": "view_count", "function": "avg"}]) for average views

3. Evaluate Video Performance

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

Metrics:
- View count
- Like/dislike ratio
- Comments count
- Watch time signals (retention)

4. Analyze Audience Engagement

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

Look for:
- Active community
- Technical discussions
- Purchase decisions influenced

Expected Output:

• 10-20 relevant channels
• Subscriber and view metrics
• Engagement analysis
• Partnership fit assessment

Use export_data(cache_key, "csv") to export channel data.

MCP Tools Reference (v2)

Instagram

• execute("instagram", "search", "search_posts", {"query": ..., "count": N}) - Find posts by keywords/hashtags
• execute("instagram", "user", "user", {"user": ...}) - Get profile with followers, bio
• execute("instagram", "user", "user_posts", {"user": ..., "count": N}) - Get recent posts with engagement
• execute("instagram", "post", "post_likes", {"post": ..., "count": N}) - Check audience authenticity
• execute("instagram", "post", "post_comments", {"post": ..., "count": N}) - Analyze engagement quality
• execute("instagram", "user", "user_friendships", {"user": ..., "count": N, "type": "followers"}) - Get followers list (for analysis)

Twitter/X

• execute("twitter", "search", "search_users", {"query": ..., "count": N}) - Find users by keywords/bio
• execute("twitter", "user", "get", {"username": ...}) - Get profile with followers, tweets
• execute("twitter", "user_tweets", "get", {"username": ...}) - Get recent tweets with engagement
• execute("twitter", "search", "search_posts", {"query": ..., "count": N}) - Find influential tweets in niche

LinkedIn

• execute("linkedin", "search", "search_users", {"keywords": ..., "count": N}) - Find professionals by keywords/title
• execute("linkedin", "user", "user", {"user": ...}) - Get complete profile (includes skills with with_skills: true)
• execute("linkedin", "post", "get_user_posts", {"user": "urn", "count": N}) - Get post history and engagement
• execute("linkedin", "user", "user_skills", {"urn": ..., "count": N}) - Verify expertise (requires URN from profile)

Note: LinkedIn connection count is returned in the profile response (connection_count field). No separate endpoint needed.

YouTube

• execute("youtube", "search", "search_videos", {"query": ..., "count": N}) - Find videos by keywords
• execute("youtube", "channel", "channel_videos", {"channel": ..., "count": N}) - Get all videos from channel
• execute("youtube", "video", "video", {"video": ...}) - Get video metrics (views, likes)
• execute("youtube", "video", "video_comments", {"video": ..., "count": N}) - Analyze audience engagement

Reddit

• execute("reddit", "search", "search_posts", {"query": ..., "count": N}) - Find influential posts in subreddits
• execute("reddit", "user", "user_posts", {"username": ..., "count": N}) - Get user's post history
• execute("reddit", "user", "user_comments", {"username": ..., "count": N}) - Analyze community engagement

Web Scraping

• execute("webparser", "parse", "parse", {"url": ...}) - Scrape any webpage for contact info, media kits, etc.

Pagination, Caching & Export

• get_page(cache_key, offset, limit) - Fetch additional pages from any execute() result
• query_cache(cache_key, conditions, sort_by, aggregate, group_by) - Filter/sort/aggregate cached data
• export_data(cache_key, "csv"|"json"|"jsonl") - Export full dataset as downloadable file

Output Formats

Chat Summary:

• Top 10 influencers with key metrics
• Engagement rate comparison
• Partnership recommendations
• Contact information found

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

• Influencer name, handle, platform
• Followers, engagement rate
• Niche, content type
• Email, website
• Fit score (1-100)

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

• Complete profile data
• All posts with engagement
• Audience demographics (if available)
• Historical metrics

Influencer Evaluation Framework

Reach Metrics

• Followers: Total audience size
• Views: Average content views
• Growth: Follower growth rate

Engagement Metrics

• Rate: Engagement / Followers
• Quality: Comment depth and relevance
• Consistency: Regular engagement patterns

Authenticity Indicators

• Audience Quality: Real vs. fake followers
• Comment Quality: Meaningful discussions
• Growth Pattern: Organic vs. purchased
• Engagement Distribution: Consistent vs. spiky

Content Quality

• Production Value: Visual/audio quality
• Originality: Unique vs. repurposed
• Consistency: Regular posting schedule
• Niche Alignment: On-brand content

Partnership Fit

• Audience Overlap: Match with target market
• Brand Alignment: Values and messaging
• Professionalism: Past partnerships, disclosure
• Availability: Contact information, responsiveness

Advanced Features

Micro-Influencer Strategy

Focus on 10k-50k followers for higher engagement:

Benefits:
- Higher engagement rates (5-10% vs. 1-3%)
- More authentic audience connections
- Lower partnership costs
- Niche expertise

Discovery approach:
- Use hashtag searches via execute("instagram", "search", "search_posts", ...)
- Use query_cache() to filter by engagement rate vs. reach
- Prioritize niche relevance over size

Multi-Platform Presence Analysis

Identify influencers active across platforms:

1. Find on Instagram/Twitter
2. Search LinkedIn for professional presence:
   execute("linkedin", "search", "search_users", {"keywords": "name"})
3. Check for YouTube channel:
   execute("youtube", "search", "search_videos", {"query": "creator name", "count": 10})
4. Look for website/blog:
   execute("webparser", "parse", "parse", {"url": "website_url"})

Benefits:
- Multiple touchpoints
- Diverse content formats
- Professional credibility
- Larger total reach

Audience Demographics Research

Analyze who follows the influencer:

Instagram:
- execute("instagram", "user", "user_friendships", {"user": "username", "count": 100, "type": "followers"})
- Analyze follower profiles for patterns
- Use query_cache(cache_key, group_by="location") to segment by geography

LinkedIn:
- Check who engages with posts
- Identify follower job titles/industries from post comments

YouTube:
- Analyze comment demographics via execute("youtube", "video", "video_comments", ...)
- Check subscriber locations (if available)

Reference Documentation

• DISCOVERY_CRITERIA.md - Influencer evaluation criteria, scoring frameworks, and niche identification strategies

Troubleshooting

No Influencers Found:

• Broaden search keywords
• Try multiple hashtags
• Search across multiple platforms
• Reduce minimum follower requirements

Low Engagement Rates:

• Use query_cache(cache_key, conditions=[{"field": "engagement_rate", "operator": ">", "value": 0.03}]) to filter
• Focus on micro-influencers (smaller = higher engagement)
• Check for bot followers (sudden spikes)

No Contact Information:

• Check bio for email/website
• Look for LinkedIn profile via execute("linkedin", "search", "search_users", {"keywords": "name"})
• Try website domain: execute("webparser", "parse", "parse", {"url": "domain"})
• Search for media kit or press page

API Errors:

• Check llm_hint in error responses for actionable guidance
• LinkedIn endpoints requiring URN: get URN from profile response first, do not guess aliases
• Use execute("linkedin", "search", "search_users", ...) to find correct aliases before fetching profiles

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