Building a Twitter Trends Analysis MCP Server for Claude
Description
# Building a Twitter Trends Analysis MCP Server for Claude This tutorial will guide you through creating a Model Context Protocol (MCP) server that connects Twitter's trending topics with Claude's analysis capabilities. The server will fetch real-time Twitter trends and use…
About
# Building a Twitter Trends Analysis MCP Server for Claude This tutorial will guide you through creating a Model Context Protocol (MCP) server that connects Twitter's trending topics with Claude's analysis capabilities. The server will fetch real-time Twitter trends and use Claude to analyze them for business…
Details
- Author
- Muralikrishankp
- Downloads
- 212
- Categories
- AI
Jump to
- Real-time Twitter trend fetching
- Category-based trend analysis
- Business opportunity identification
- AI‑powered insights via Claude
- Detailed logging for debugging
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Building a Twitter Trends Analysis MCP Server for ClaudeCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Set up a Python 3.8+ virtual environment, install dependencies (tweepy, mcp, python-dotenv, hatchling), obtain Twitter API credentials, configure the server files, and update Claude Desktop’s config (claude_desktop_config.json) to point to the server’s entry point (twitter_server_run.py). Run the server with python twitter_server_run.py and invoke it in Claude Desktop by asking, for example, “Analyze current Twitter trends for SaaS opportunities.”
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"building a twitter trends analysis mcp server for claude": {
"Twitter-MCP-Server-for-Claude": {
"command": "python",
"args": [
"-m",
"venv",
".venv"
]
}
}
}
}McpServers
{
"Twitter-MCP-Server-for-Claude": {
"command": "python",
"args": [
"-m",
"venv",
".venv"
]
}
}Building a Twitter Trends Analysis MCP Server for Claude
This tutorial will guide you through creating a Model Context Protocol (MCP) server that connects Twitter's trending topics with Claude's analysis capabilities. The server will fetch real-time Twitter trends and use Claude to analyze them for business opportunities.
Prerequisites
- Python 3.8 or higher
- Claude Desktop installed
- Twitter Developer Account with API access
- Basic understanding of Python
Part 1: Setting Up the Environment
1. Create a new project directory:
mkdir twitter-trends-mcp
cd twitter-trends-mcp
2. Set up a virtual environment:
python -m venv .venv
.venv\Scripts\activate # On Windows
3. Install required packages:
pip install tweepy mcp python-dotenv hatchling
Part 2: Project Structure
Create the following directory structure:
twitter-trends-mcp/
├── pyproject.toml
├── twitter_server_run.py
├── src/
│ └── twitter_trends_mcp/
│ ├── __init__.py
│ └── server.py
Part 3: Configuration Files
1. Create pyproject.toml in the root directory:
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[project]
name = "twitter-trends-mcp"
version = "0.1.0"
description = "Twitter Trends MCP Server"
requires-python = ">=3.8"
dependencies = [
"tweepy",
"mcp",
"python-dotenv"
]
[tool.hatch.build]
packages = ["src/twitter_trends_mcp"]
include = ["src/twitter_trends_mcp/*"]
[project.scripts]
twitter-trends-server = "twitter_trends_mcp:main"
2. Create src/twitter_trends_mcp/__init__.py:
"""Twitter Trends MCP Server package."""
import asyncio
from . import server
def main():
"""Main entry point for the package."""
asyncio.run(server.main())
__all__ = ['main', 'server']
3. Create entry point file twitter_server_run.py:
```python
#!/usr/bin/env python
import os
import sys
import logging
from pathlib import Path
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