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kukapay/twitter-username-changes-mcp

by Various

An MCP server that tracks the historical changes of Twitter usernames.

K

MCP

kukapay/twitter-username-changes-mcp

Added 1 June 2026

Overview

An MCP server that tracks historical changes of Twitter usernames. It provides a tool for developers to query past username changes for a given Twitter handle. Built in Python, it uses the Model Context Protocol to integrate with AI assistants.

Best for

Best for
Developers building AI assistants that need to track Twitter username changes.

Use cases

  • Query the history of username changes for a Twitter account.
  • Integrate username change tracking into an AI assistant workflow.
  • Verify account identity by checking past usernames.

Notes

An MCP server that tracks historical changes of Twitter usernames. It provides a tool for developers to query past username changes for a given Twitter handle. Built in Python, it uses the Model Context Protocol to integrate with AI assistants.

3 stars on GitHub. Last updated 2025-04-14. Licensed MIT.

Use cases

  • Query the history of username changes for a Twitter account.
  • Integrate username change tracking into an AI assistant workflow.
  • Verify account identity by checking past usernames.

Pros

  • Simple MCP interface for easy integration with AI tools.
  • Lightweight Python server with minimal dependencies.
  • Provides historical data that is not easily accessible via Twitter API.

Cons

  • Limited to 3 stars on GitHub, indicating low community adoption.
  • May rely on unofficial data sources or scraping, which could be unreliable.
  • No clear documentation on data accuracy or update frequency.

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Simple MCP interface for easy integration with AI tools.
  • Lightweight Python server with minimal dependencies.
  • Provides historical data that is not easily accessible via Twitter API.

Cons

  • Limited to 3 stars on GitHub, indicating low community adoption.
  • May rely on unofficial data sources or scraping, which could be unreliable.
  • No clear documentation on data accuracy or update frequency.