Twitter MCP Server

Instructions

Implementation Reference

• src/handlers/socialdata/user.handlers.ts:116-179 (handler)

  Core handler function implementing the userInfluenceMetrics tool. Fetches user profile and recent tweets using socialDataClient, calculates engagement metrics (avg likes, retweets, replies, rate) and reach metrics (follower base, influence score).
  export const handleUserInfluenceMetrics: SocialDataHandler<UserInfluenceMetricsArgs> = async ( _client: any, { username, analyzeEngagement = true, analyzeReach = true }: UserInfluenceMetricsArgs ) => { try { const socialClient = getSocialDataClient(); if (!socialClient) { return createMissingApiKeyResponse('User Influence Metrics'); } // Get user profile and recent tweets const [profile, tweets] = await Promise.all([ socialClient.getUserProfile({ username, includeMetrics: true }), socialClient.getUserTweets({ username, maxResults: 20 }) ]); const user = profile.data; const recentTweets = tweets.data || []; // Calculate influence metrics const metrics: any = { user: { username: user.username, followers: user.public_metrics?.followers_count || 0, following: user.public_metrics?.following_count || 0, verified: user.verified || false } }; if (analyzeEngagement && recentTweets.length > 0) { const totalLikes = recentTweets.reduce((sum: number, tweet: any) => sum + (tweet.public_metrics?.like_count || 0), 0); const totalRetweets = recentTweets.reduce((sum: number, tweet: any) => sum + (tweet.public_metrics?.retweet_count || 0), 0); const totalReplies = recentTweets.reduce((sum: number, tweet: any) => sum + (tweet.public_metrics?.reply_count || 0), 0); metrics.engagement = { avg_likes_per_tweet: Math.round(totalLikes / recentTweets.length), avg_retweets_per_tweet: Math.round(totalRetweets / recentTweets.length), avg_replies_per_tweet: Math.round(totalReplies / recentTweets.length), engagement_rate: user.public_metrics?.followers_count > 0 ? Math.round(((totalLikes + totalRetweets + totalReplies) / recentTweets.length / user.public_metrics.followers_count) * 10000) / 100 : 0 }; } if (analyzeReach) { metrics.reach = { follower_base: user.public_metrics?.followers_count || 0, potential_reach: user.public_metrics?.followers_count || 0, estimated_influence_score: Math.min(100, Math.log10((user.public_metrics?.followers_count || 1) + 1) * 20) }; } return createSocialDataResponse( formatAnalytics(metrics, `Influence Metrics for @${username}`) ); } catch (error) { throw new Error(formatSocialDataError(error as Error, 'user influence metrics')); } };
• src/socialdata-tools.ts:153-173 (registration)

  Tool registration definition including description and input schema for the userInfluenceMetrics MCP tool.
  userInfluenceMetrics: { description: 'Calculate user influence scores and engagement metrics', inputSchema: { type: 'object', properties: { username: { type: 'string', description: 'Username to analyze influence metrics for' }, analyzeEngagement: { type: 'boolean', description: 'Include engagement analysis (default: true)' }, analyzeReach: { type: 'boolean', description: 'Include reach and influence scoring (default: true)' } }, required: ['username'] } },
• src/types/socialdata.ts:52-56 (schema)

  TypeScript interface defining the input arguments for the userInfluenceMetrics handler.
  export interface UserInfluenceMetricsArgs { username: string; analyzeEngagement?: boolean; analyzeReach?: boolean; }
• src/index.ts:451-454 (registration)

  Dispatch/registration in the main CallToolRequestSchema handler that routes userInfluenceMetrics calls to the specific handler function.
  case 'userInfluenceMetrics': { const args = request.params.arguments as any; response = await handleUserInfluenceMetrics(client, args); break;
• src/index.ts:73-73 (registration)

  Import statement for the handleUserInfluenceMetrics handler used in tool dispatching.
  handleUserInfluenceMetrics,