MCP-Twikit: An MCP Server for Interacting with Twitter for Social Media Analysis and Data Retrieval 210

MCP-Twikit What is it?

MCP-Twikit is a server based on the Model Context Protocol, specifically designed to interact with Twitter. It allows users to access Twitter's data via the MCP protocol, helping you search tweets, browse content by timeline, analyze tweets with LLMs, track replies and retweets, and use it for tasks such as brand monitoring, competitor analysis, and public opinion research.

MCP-Twikit Features

• Sentiment Analysis: It can compare the sentiment of tweets from different Twitter accounts. For example, by analyzing the sentiment of tweets from several Indonesian internet service providers, you can discover that they all have issues such as unstable networks and slow customer support responses.

• Tweet Retrieval: It can retrieve tweets from a specific account and sort them by latest, hottest, and other methods.

Installation

Install via Smithery:

npx -y @smithery/cli install mcp-twikit --client claude

Manual Installation:

{
"mcpServer": {
"command": "uvx",
"args": ["--from", "git+https://github.com/adhikasp/mcp-twikit", "mcp-twikit"],
"env": {
  "TWITTER_USERNAME": "@example",
  "TWITTER_EMaiL": "[email protected]",
  "TWITTER_PASSWORD": "secret"
}
}
}

Usage Example

The following is an example of using MCP-Twikit to analyze the sentiment of tweets from different accounts:

$ llm compare 20 latest tweet directed @IndiHomeCare, @di_cbn, @BiznetHome, @ID_MyRepublic. What are people sentiment to the product? Do 1 search for each account

This command will invoke MCP-Twikit's search_twitter function to retrieve the latest 20 tweets from these four accounts and analyze the public sentiment toward these internet service providers.

MCP-Twikit Applications

• Sentiment Analysis: By analyzing tweets on Twitter, understand the public's sentiment toward specific brands, products, or events. For example, you can compare the sentiment of tweets from different internet service providers to find out user satisfaction with their services.

• Trend Analysis: Retrieve tweets about a specific topic or hashtag, analyze their popularity and trend changes, helping brands or individuals understand market dynamics.

• User Behavior Analysis: Study the tweeting behavior of specific users or groups, including posting time, frequency, content type, etc., to better understand the target audience.

• Tweet Retrieval: Supports retrieving tweets by keyword, user, hashtag, and more, with results sortable by latest, hottest, etc.

• Timeline Management: Access and manage a user's Twitter timeline, including fetching the latest tweets, deleting old ones, and other operations.

• Data Export: Export retrieved tweet data into formats such as CSV for further analysis and processing.

As an MCP server, MCP-Twikit can integrate with various AI models, such as Claude, enabling AI to directly access and analyze Twitter data. Combined with other MCP tools, it can automate tasks such as periodically retrieving tweets on specific topics and generating reports.

GitHub:https://github.com/adhikasp/mcp-twikit