twikit-mcp
Related Servers
Alternatives to twikit-mcp
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityDmaintenanceAn MCP server for interacting with the Twitter/X API v2, enabling AI assistants to retrieve tweets, post content, reply, quote, and more programmatically.1,734 npm13MIT
- FlicenseAqualityDmaintenanceAn MCP server that provides AI agents with full access to the X (Twitter) API for posting, searching, and managing engagement through natural language. It supports comprehensive tools for tweet management, media uploads, and account analytics across multiple MCP-compatible clients.1554-
- AlicenseNot gradedqualityDmaintenanceAn MCP server that enables AI agents to automate actions on X (Twitter) through a real browser session, including posting, engaging, and reading via over 40 tools. It supports self-hosting and provides a panel for API key management.07MIT
- AlicenseAqualityCmaintenanceMCP server that exposes twitterapi.io endpoints as tools for AI agents, enabling reading tweets, mentions, thread context, and writing tweets with cookie-based authentication.421 npmMIT
- AlicenseAqualityCmaintenanceMCP server for Twitter/X enabling AI agents to search, post, reply, and engage with tweets.147 npm2MIT
- AlicenseNot gradedqualityCmaintenanceAn MCP server that gives an AI agent read and write access to X (Twitter) via the internal web API, requiring no paid API key.10 npm1AGPL 3.0
TDQS
Scored across 27 tools
Each tool targets a distinct resource+action combination, such as get_user vs get_user_by_id vs search_users, which are clearly separated by lookup key. Opposite actions like like/unlike and retweet/undo_retweet are unambiguous, leaving no confusion about tool purpose.
The set largely follows a consistent verb_noun pattern, e.g., search_users, get_tweet, post_tweet, like_tweet. Minor deviations like whoami and rate_limit_status, plus inconsistent DM naming (get_dm_history vs send_direct_message), slightly weaken the pattern but it remains readable.
With 27 tools, the set is above the 25-tool threshold, feeling heavy and potentially overwhelming for an agent. While each tool has a purpose, the count is excessive for a focused client, and some operations could be merged or omitted.
The core Twitter workflows are well covered, including tweets, users, follows, likes, retweets, DMs, and trends. However, missing reverse operations like unbookmark_tweet and the lack of a get_user_likes method create minor dead ends for common interaction patterns.