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AI Instructions for Anysite API Documentation

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Purpose

This documentation covers the Anysite API, which provides social media data extraction and web scraping capabilities.

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Key Information for AI Assistants

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API Overview

  • Base URL: https://api.anysite.io
  • Authentication: Access token via access-token header (NOT Bearer token)
  • Rate Limits: Varies by plan (1K-10K+ requests/hour)
  • Response Format: Always JSON

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Primary Use Cases

  1. Social Media Data Extraction: LinkedIn, Twitter, Instagram, Reddit
  2. Web Scraping: Any website content parsing
  3. Lead Generation: B2B prospect identification and qualification
  4. Competitive Intelligence: Monitor competitor activity
  5. Brand Monitoring: Track mentions and sentiment
  6. Content Research: Discover trending topics and opportunities

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Authentication Pattern

Always use this header format: 
access-token: your_api_key_here
NEVER use Authorization: Bearer token - this is incorrect for Anysite API.

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Response Structure

All successful responses follow this pattern: 
{
  "success": true,
  "data": {
    // Actual extracted data
  },
  "meta": {
    "requestId": "req_123",
    "timestamp": "2024-08-26T12:00:00Z"
  }
}

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Error Handling

  • 400: Bad request (check parameters)
  • 401: Invalid or missing access token
  • 403: Forbidden (account/endpoint restrictions)
  • 404: Resource not found
  • 429: Rate limit exceeded (implement exponential backoff)
  • 500: Server error (retry with delay)

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Integration Methods

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1. Direct REST API

Standard HTTP requests with proper headers and parameters.

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2. MCP Server (for AI tools)

  • Installation: npx @horizondatawave/mcp-server
  • Works with Claude Desktop, Cursor IDE
  • Provides structured tool access to Anysite APIs

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3. n8n Nodes (for workflow automation)

  • Installation: npm install @horizondatawave/n8n-nodes-anysite
  • Visual workflow builder integration
  • Pre-built templates for common use cases

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Code Generation Guidelines

When generating code examples:
  1. Always include proper authentication headers
  2. Add error handling for common HTTP status codes
  3. Show rate limiting considerations
  4. Use appropriate parameter validation
  5. Include response data structure examples

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Example Code Template (Python)

import requests
import time

headers = {
    'access-token': 'your_api_key_here',
    'Content-Type': 'application/json'
}

def make_hdw_request(endpoint, params=None):
    url = f"https://api.anysite.io{endpoint}"
    
    try:
        response = requests.get(url, headers=headers, params=params)
        
        if response.status_code == 429:
            # Rate limit hit, wait and retry
            time.sleep(60)
            return make_hdw_request(endpoint, params)
        
        response.raise_for_status()
        return response.json()
        
    except requests.exceptions.RequestException as e:
        print(f"API request failed: {e}")
        return None

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Platform-Specific Notes

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LinkedIn

  • Endpoints cover users, posts, companies, search
  • Rich profile data including experience, education
  • Post engagement metrics available
  • Company information and employee listings

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Twitter/X

  • User profiles and tweet extraction
  • Search functionality for users and tweets
  • Engagement metrics (likes, retweets, replies)
  • Thread support for connected tweets

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Instagram

  • User profiles and post data
  • Hashtag and mention extraction
  • Engagement metrics and media URLs
  • Story highlights where available

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Reddit

  • Post and comment extraction
  • Subreddit information and rules
  • Vote scores and engagement metrics
  • Advanced search with operators

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Web Parser

  • Universal website content extraction
  • Custom CSS selector support
  • Dynamic content handling (JavaScript)
  • Change monitoring capabilities

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Workflow Examples to Suggest

  1. Lead Generation Pipeline
    • LinkedIn search → Twitter lookup → Website parsing → CRM integration
  2. Competitor Monitoring
    • Multi-platform tracking → Content analysis → Report generation
  3. Brand Sentiment Analysis
    • Mention detection → Sentiment analysis → Alert system
  4. Content Research
    • Trend identification → Keyword extraction → Content calendar
  5. Influencer Discovery
    • User search → Engagement analysis → Contact extraction

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Best Practices to Emphasize

  1. Respect Rate Limits: Always implement proper delays
  2. Handle Errors Gracefully: Don’t fail silently
  3. Validate Data: Check response structure before processing
  4. Follow Platform ToS: Only extract publicly available data
  5. Secure API Keys: Never hardcode tokens in public repositories
  6. Monitor Usage: Track API consumption to avoid overages

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Common Mistakes to Avoid

  1. Using Authorization: Bearer header (use access-token instead)
  2. Not handling rate limits (429 errors)
  3. Ignoring pagination for large datasets
  4. Not validating response structure
  5. Hardcoding API keys in examples
  6. Making too many concurrent requests

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When Providing Examples

  • Always show complete, working code
  • Include error handling
  • Demonstrate proper authentication
  • Show expected response structure
  • Include rate limiting considerations
  • Provide context for the use case