anysite-influencer-discovery

name

anysite-influencer-discovery

description

Discover and analyze influencers across Instagram, Twitter/X, LinkedIn, YouTube, and Reddit using anysite MCP server. Find content creators by niche, analyze engagement metrics, evaluate audience quality, track influencer activity, and identify partnership opportunities. Supports multi-platform influencer search, profile enrichment, follower analysis, and engagement tracking. Use when users need to find brand ambassadors, research content creators, identify thought leaders, build influencer lists, or evaluate influencer partnerships for marketing campaigns.

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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