Instagram Research MCP

A user-outcome-focused MCP server for systematic Instagram competitor research and outlier content discovery, designed following Anthropic's agent tool principles and IG Profit methodology.

Design Philosophy

This MCP server consolidates 18 generic Instagram tools into 3 workflow-specific research operations, prioritizing user outcomes over technical capabilities.

Key Principles

• User-Outcome Focus: Tools designed around systematic competitor research workflow
• Zero Hallucination: Comprehensive parameter documentation with usage examples
• Token Efficiency: Semantic responses with consolidated operations
• Agent Optimization: Reduced cognitive load through workflow-specific tools

Core Functions

1. discover_accounts_by_hashtag

Systematically discover Instagram competitors by hashtag with follower filtering.

Replaces manual process: Hashtag searching → account filtering → competitor identification

// Example: Find medium-sized affiliate marketing accounts
{
  "hashtag": "affiliatemarketing",
  "min_followers": 5000,
  "max_followers": 50000,
  "limit": 30,
  "sort_by": "engagement_rate"
}

2. analyze_account_outliers

Automatically detect 5x+ performing content for IG Profit "stealing like artists" methodology with deep content discovery.

Replaces manual process: Content review → baseline calculation → outlier identification

// Example: Deep viral content analysis
{
  "username": "jonathan_montoya24",
  "outlier_multiplier": 5,
  "time_range_days": 30,
  "max_page_depth": 5,
  "min_outlier_results": 8,
  "content_limit": 50
}

Enhanced Parameters:

• max_page_depth: Multiplies content search depth (e.g., 5 = 250 posts with 50 content_limit)
• min_outlier_results: Target number of viral posts to discover
• Automatically searches deeper content for comprehensive outlier discovery

3. batch_account_analysis

Process multiple competitors systematically with deep content discovery and CSV export for workflow integration.

Replaces manual process: Individual analysis → comparative review → data export

// Example: Deep batch competitor analysis
{
  "usernames": ["jonathan_montoya24", "charlie_chang", "alex_hormozi"],
  "analysis_type": "outliers",
  "export_format": "csv",
  "max_page_depth": 4,
  "outlier_multiplier": 3,
  "time_range_days": 30
}

Enhanced Parameters:

• max_page_depth: Multiplies content search per account (e.g., 4 = 200 posts per competitor)
• Consistent deep analysis across entire competitor set
• Comprehensive outlier discovery for systematic competitive research

Installation

npm install @andrewlwn77/instagram-research-mcp

Configuration

1. Copy environment template:

cp .env.example .env

2. Add your Instagram Social API key:

INSTAGRAM_SOCIAL_API_KEY=your_rapidapi_key_here

Get your API key from: Instagram Social API

Usage Examples

Competitor Discovery

# Find 30 medium-sized affiliate marketing accounts
discover_accounts_by_hashtag(
  hashtag="affiliatemarketing",
  min_followers=5000,
  max_followers=50000,
  limit=30,
  sort_by="engagement_rate"
)

Outlier Content Analysis

# Deep analysis for comprehensive viral content discovery
analyze_account_outliers(
  username="jonathan_montoya24",
  outlier_multiplier=5,
  time_range_days=30,
  max_page_depth=5,
  min_outlier_results=8
)

Batch Competitor Analysis

# Deep batch analysis across multiple competitors
batch_account_analysis(
  usernames=["account1", "account2", "account3"],
  analysis_type="outliers",
  export_format="csv",
  max_page_depth=4,
  outlier_multiplier=3
)

Integration with IG Profit Methodology

This MCP server is designed to work with the IG Profit "modeling/stealing like artists" methodology:

1. Phase 1: Systematic competitor discovery by niche hashtags
2. Phase 2: Outlier content identification (5x+ performance)
3. Phase 3: Batch analysis for comparative research

Zero-Hallucination Design

Every parameter includes:

• Clear description with business context
• 3+ concrete usage examples
• Realistic competitor names and scenarios
• Enum constraints where applicable

This prevents parameter guessing and ensures consistent agent performance.

Architecture

Built on proven Instagram Social MCP foundation with:

• Consolidated workflow-specific operations
• Semantic natural language responses
• Token-efficient output formatting
• Comprehensive error handling

Requirements

• Node.js >=18.0.0
• Instagram Social API key (RapidAPI)
• Compatible with existing IG Profit workflow and CSV formats

License

MIT