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ethan-tsai-tsai

Thread Analyzer MCP Server

by ethan-tsai-tsai
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

What is this?

An MCP server that lets your AI assistant scrape a Threads post URL, then query and analyze the replies — all through natural conversation.

Instead of:

1. Manually open browser
2. Scroll through hundreds of replies
3. Copy-paste into spreadsheet
4. Manually look for patterns

Just say:

"Analyze the replies on this Threads post: https://www.threads.com/@zuck/post/ABC123"

Your AI agent handles the rest.

Related MCP server: AgentReacher

Features

  • Network interception — Captures GraphQL API responses, not fragile CSS selectors that Meta randomizes

  • Anti-detection — Randomized scroll delays, stealth browser flags, custom User-Agent

  • 4 MCP tools — Scrape, list, search, and get statistics on replies

  • Works with any MCP client — Claude Code, Claude Desktop, Cursor, VS Code, Windsurf, and more

MCP Tools

Tool

Description

scrape_thread(url)

Scrape all replies from a public Threads post

get_all_replies()

Return all scraped replies with username and timestamp

search_replies(keyword)

Case-insensitive keyword search across replies

get_reply_stats()

Reply count, top commenters, avg length, time range

Quick Start

Prerequisites

  • Python 3.13+

  • uv package manager

Installation

git clone https://github.com/ethan-tsai-tsai/thread-analyzer.git
cd thread-analyzer
uv sync
uv run playwright install chromium

Configuration

Add the server to your MCP client config:

{
  "mcpServers": {
    "thread-analyzer": {
      "command": "uv",
      "args": ["run", "--directory", "/absolute/path/to/thread-analyzer", "python", "server.py"]
    }
  }
}

Add to your project's .mcp.json:

{
  "mcpServers": {
    "thread-analyzer": {
      "command": "uv",
      "args": ["run", "--directory", "/absolute/path/to/thread-analyzer", "python", "server.py"]
    }
  }
}

Or run: claude mcp add thread-analyzer -- uv run --directory /absolute/path/to/thread-analyzer python server.py

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "thread-analyzer": {
      "command": "uv",
      "args": ["run", "--directory", "/absolute/path/to/thread-analyzer", "python", "server.py"]
    }
  }
}

Go to Cursor Settings > MCP > Add new MCP Server, then add:

{
  "mcpServers": {
    "thread-analyzer": {
      "command": "uv",
      "args": ["run", "--directory", "/absolute/path/to/thread-analyzer", "python", "server.py"]
    }
  }
}

Add to .vscode/mcp.json in your workspace:

{
  "servers": {
    "thread-analyzer": {
      "command": "uv",
      "args": ["run", "--directory", "/absolute/path/to/thread-analyzer", "python", "server.py"]
    }
  }
}

Add to ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "thread-analyzer": {
      "command": "uv",
      "args": ["run", "--directory", "/absolute/path/to/thread-analyzer", "python", "server.py"]
    }
  }
}

Standalone CLI

You can also use the scraper directly without MCP:

uv run python scraper.py "https://www.threads.com/@user/post/XXXXX"

# Options
uv run python scraper.py "URL" --output custom.csv --max-scrolls 50

How It Works

┌─────────────┐     MCP (stdio)     ┌──────────────┐    Playwright    ┌─────────────┐
│  AI Client  │ ◄──────────────────► │  server.py   │ ◄──────────────► │  Threads.com │
│ (Claude,    │   scrape_thread()    │  (FastMCP)   │   GraphQL API   │  (Meta)      │
│  Cursor...) │   get_all_replies()  │              │   interception  │              │
│             │   search_replies()   │  replies.csv │                 │              │
│             │   get_reply_stats()  │              │                 │              │
└─────────────┘                      └──────────────┘                 └─────────────┘

  1. You give your AI assistant a Threads post URL

  2. AI calls scrape_thread(url) via MCP

  3. Server launches headless Chromium, navigates to the post

  4. Playwright intercepts GraphQL network responses containing reply data

  5. Server parses replies (username, text, timestamp), saves to CSV

  6. AI uses get_all_replies(), search_replies(), get_reply_stats() to analyze

Anti-Detection

Technique

Purpose

Custom User-Agent

Mimics real Chrome browser

navigator.webdriver removal

Hides automation flag

AutomationControlled disabled

Prevents Chromium detection

Randomized scroll delays (1.5-4.5s)

Avoids behavioral fingerprinting

Early stop on idle scrolls

Mimics natural browsing patterns

Limitations

  • Public posts only — Cannot access private or restricted posts

  • Meta's anti-bot measures — Meta may block headless browsers; if scraping fails, try the standalone CLI in non-headless mode

  • GraphQL schema changes — Meta periodically changes their API structure; the parser in scraper.py may need updating

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

License

MIT

This server cannot be installed

A
license - permissive license
-
quality - not tested
D
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity

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