Social Media MCP Server

# System Patterns: Social Media MCP Server ## System Architecture The Social Media MCP Server follows a modular, component-based architecture designed for extensibility, reliability, and maintainability. The system is structured around the following architectural patterns: ```mermaid flowchart TD Client[Client] -->|Natural Language Request| MCP[MCP Server Interface] MCP --> Core[Core Orchestrator] subgraph Components NLP[NLP Processor] Conversation[Conversation Manager] History[History Manager] Research[Research Engine] Content[Content Generator] Format[Platform Formatter] Post[Posting Service] Analytics[Analytics Engine] RateLimit[Rate Limit Manager] end Core --> NLP NLP --> Conversation Conversation --> Research Research --> Content Content --> Format Format --> Post Post --> Analytics Post --> History History -.->|Checks| NLP RateLimit -.->|Controls| Research RateLimit -.->|Controls| Content RateLimit -.->|Controls| Post Analytics -.->|Feedback| Content Analytics -.->|Feedback| Format subgraph External Services Twitter[Twitter API] Mastodon[Mastodon API] LinkedIn[LinkedIn API] BraveSearch[Brave Search] Perplexity[Perplexity] AIModels[AI Models] end Research -->|Uses| BraveSearch Research -->|Uses| Perplexity Content -->|Uses| AIModels Post -->|Posts to| Twitter Post -->|Posts to| Mastodon Post -->|Posts to| LinkedIn Analytics -->|Collects from| Twitter Analytics -->|Collects from| Mastodon Analytics -->|Collects from| LinkedIn ``` ## Key Design Patterns ### 1. Modular Component Architecture The system is divided into loosely coupled components that communicate through well-defined interfaces. This allows for: - Independent development and testing of components - Easy replacement or upgrade of individual components - Addition of new components (e.g., new social media platforms) ### 2. Strategy Pattern Used for implementing different strategies for: - Content generation (different AI models) - Platform formatting (platform-specific rules) - Research methods (different data sources) ```typescript interface ContentGenerationStrategy { generateContent(intent: UserIntent, research: ResearchData): Promise<Content>; } class AnthropicStrategy implements ContentGenerationStrategy { async generateContent(intent: UserIntent, research: ResearchData): Promise<Content> { // Implementation using Anthropic API } } class OpenAIStrategy implements ContentGenerationStrategy { async generateContent(intent: UserIntent, research: ResearchData): Promise<Content> { // Implementation using OpenAI API } } ``` ### 3. Factory Pattern Used to create appropriate instances of: - Platform connectors - Research providers - Content generators ```typescript class PlatformConnectorFactory { static createConnector(platform: SocialPlatform, credentials: Credentials): PlatformConnector { switch (platform) { case SocialPlatform.Twitter: return new TwitterConnector(credentials); case SocialPlatform.Mastodon: return new MastodonConnector(credentials); case SocialPlatform.LinkedIn: return new LinkedInConnector(credentials); default: throw new Error(`Unsupported platform: ${platform}`); } } } ``` ### 4. Observer Pattern Used for: - Monitoring rate limits - Tracking API usage - Collecting analytics data - Notifying components of state changes ```typescript class RateLimitMonitor { private observers: RateLimitObserver[] = []; addObserver(observer: RateLimitObserver): void { this.observers.push(observer); } notifyApproachingLimit(api: string, remainingCalls: number): void { this.observers.forEach(observer => observer.onApproachingLimit(api, remainingCalls)); } } ``` ### 5. Command Pattern Used for: - Queuing API requests - Implementing retry logic - Managing scheduled posts ```typescript interface ApiCommand { execute(): Promise<any>; getRetryCount(): number; incrementRetryCount(): void; getPriority(): number; } class PostToTwitterCommand implements ApiCommand { constructor(private content: Content, private credentials: TwitterCredentials) {} async execute(): Promise<any> { // Implementation for posting to Twitter } // Other methods... } ``` ### 6. Adapter Pattern Used to provide a unified interface for: - Different social media APIs - Various AI model providers - Research data sources ```typescript interface SocialMediaAdapter { post(content: Content): Promise<PostResult>; getEngagement(postId: string): Promise<EngagementMetrics>; } class TwitterAdapter implements SocialMediaAdapter { // Implementation for Twitter API } class MastodonAdapter implements SocialMediaAdapter { // Implementation for Mastodon API } class LinkedInAdapter implements SocialMediaAdapter { // Implementation for LinkedIn API } ``` ### 7. Repository Pattern Used for: - Storing and retrieving analytics data - Caching research results - Managing user preferences ```typescript interface AnalyticsRepository { savePostMetrics(postId: string, platform: string, metrics: EngagementMetrics): Promise<void>; getPostMetrics(postId: string, platform: string): Promise<EngagementMetrics>; getPerformanceByTimeRange(platform: string, startTime: Date, endTime: Date): Promise<PerformanceData[]>; } ``` ## Component Relationships ### Core Orchestrator - Central component that coordinates the workflow - Manages the lifecycle of a content request - Delegates to specialized components - Handles error recovery and retries ### NLP Processor - Parses natural language instructions - Extracts intent, tone, and content requirements - Identifies research needs - Provides structured data for the Research Engine ### Research Engine - Coordinates research across multiple sources - Uses Brave Search and Perplexity MCPs - Aggregates and filters research results - Provides relevant data for content generation ### Content Generator - Selects appropriate AI models based on content needs - Manages fallback between models - Generates platform-appropriate content - Creates variations for A/B testing ### Platform Formatter - Applies platform-specific formatting rules - Handles character limits and content truncation - Creates thread structures when needed - Optimizes media attachments ### Posting Service - Manages authentication with platforms - Handles the actual posting process - Implements retry logic for failed posts - Provides posting status updates ### Rate Limit Manager - Monitors API usage across all components - Implements token bucket algorithm for rate limiting - Manages request queuing and prioritization - Provides fallback mechanisms ### Conversation Manager - Manages multi-turn conversations with users - Generates follow-up questions based on missing context - Maintains conversation state using a state machine - Updates user intent based on answers to questions ### History Manager - Stores and retrieves post history - Detects similar posts to avoid repetition - Tracks post performance over time - Provides insights for content optimization ### Analytics Engine - Collects engagement metrics from platforms - Analyzes content performance - Identifies patterns and trends - Provides feedback for content optimization ## Technical Decisions ### 1. TypeScript for Type Safety - Using TypeScript for strong typing and better developer experience - Enables better code organization and maintainability - Provides compile-time error checking ### 2. MCP SDK for Server Implementation - Leveraging the Model Context Protocol SDK for server implementation - Provides standardized communication with AI assistants - Enables tool and resource registration ### 3. Multi-model AI Approach - Using multiple AI models for different aspects of content generation - Provides fallback options for rate limit handling - Allows specialized models for specific tasks ### 4. Caching Strategy - Implementing caching for research results and API responses - Reduces duplicate API calls - Improves performance and helps with rate limit management ### 5. Asynchronous Processing - Using asynchronous processing for non-blocking operations - Implements request queuing for rate-limited operations - Provides better user experience during high-load periods ### 6. Structured Logging - Implementing comprehensive logging throughout the system - Enables debugging and performance monitoring - Provides insights for system optimization ### 7. Error Handling Strategy - Implementing robust error handling with appropriate recovery mechanisms - Using circuit breaker pattern for external service failures - Providing clear error messages and recovery options