Social Media MCP Server

social-media-mcp
memory-bank
# Progress: Social Media MCP Server

## What Works

We have made significant progress in implementing the Social Media MCP Server:

- Created the project structure and Memory Bank documentation
- Implemented the basic MCP server structure with tool definitions
- Developed a rate limit management system
- Created the NLP processor for intent extraction
- Implemented content generation strategies using OpenAI and Anthropic
- Added Twitter, Mastodon, and LinkedIn client integrations
- Implemented research capabilities with Brave Search MCP
- Implemented in-depth research with Perplexity MCP
- Added conversation management for interactive questioning
- Implemented post history tracking to avoid repetition
- Added robust error handling and fallback mechanisms for API failures
- Implemented debug mode for detailed logging of API requests and responses
- Integrated with Cline MCP settings for Brave Search

## What's Left to Build

### Phase 1: Setup and Core Infrastructure
- [x] Basic MCP server structure
- [x] Secure credential management
- [x] Rate limit management system
- [x] Core orchestrator component
- [x] Project configuration and environment setup

### Phase 2: Platform Integration
- [x] Twitter API integration
  - [x] Authentication
  - [x] Posting capabilities
  - [ ] Media upload
  - [ ] Analytics retrieval
- [x] Mastodon API integration
  - [x] Authentication
  - [x] Posting capabilities
  - [ ] Media upload
  - [ ] Analytics retrieval
- [x] LinkedIn API integration
  - [x] Authentication
  - [x] Posting capabilities
  - [ ] Media upload
  - [ ] Analytics retrieval

### Phase 3: Research Capabilities
- [x] Brave Search MCP integration
  - [x] MCP tool integration
  - [x] Search query generation
  - [x] Result extraction and processing
- [x] Perplexity MCP integration
  - [x] MCP tool integration
  - [x] Research query generation
  - [x] Result extraction and processing
  - [x] Fallback to mock implementation
- [x] Research aggregation system
  - [x] Result filtering and ranking
  - [x] Information extraction
  - [ ] Caching mechanism

### Phase 4: Content Generation
- [x] Natural language processing
  - [x] Intent extraction
  - [x] Tone analysis
  - [x] Content requirements identification
- [x] Multi-model content generation
  - [x] Model selection logic
  - [x] Fallback mechanisms
  - [ ] Content quality assessment
- [x] Platform-specific formatting
  - [x] Twitter formatting
  - [x] Mastodon formatting
  - [x] Thread creation
  - [ ] Media handling

### Phase 5: Analytics and Optimization
- [ ] Analytics collection
  - [ ] Engagement metrics retrieval
  - [ ] Performance tracking
- [ ] Content optimization
  - [ ] Performance analysis
  - [ ] Strategy adjustment
  - [ ] A/B testing
- [x] Scheduling capabilities
  - [ ] Optimal time determination
  - [x] Post scheduling
  - [ ] Queue management

## Current Status

**Overall Status**: Phase 4 - Content Generation (Partially Complete)

### Component Status

| Component | Status | Notes |
|-----------|--------|-------|
| Project Structure | Complete | Directory structure and file organization established |
| MCP Server | Complete | Basic server with tool definitions implemented |
| Rate Limit Manager | Complete | Token bucket implementation with request queuing |
| Twitter Integration | Complete | Authentication, posting, and trending topics with fallback mechanisms |
| Mastodon Integration | Partial | Authentication and posting implemented |
| LinkedIn Integration | Complete | Authentication, posting, and trending topics implemented |
| Research Engine | Complete | Brave Search and Perplexity MCP integration complete |
| Content Generator | Complete | OpenAI and Anthropic strategies implemented |
| Platform Formatter | Complete | Twitter, Mastodon, and LinkedIn formatting implemented |
| Conversation Manager | Complete | Interactive questioning system implemented |
| History Manager | Complete | Post history tracking implemented |
| Analytics Engine | Not Started | Planned for Phase 5 |
| Error Handling | Complete | Robust error handling with fallback mechanisms |
| Debugging | Complete | Debug mode for detailed logging of API requests and responses |

### Timeline Progress

- [x] Project planning and architecture design
- [x] Phase 1: Setup and Core Infrastructure
- [x] Phase 2: Platform Integration (Basic functionality)
- [x] Phase 3: Research Capabilities (Basic functionality)
- [x] Phase 4: Content Generation (Basic functionality)
- [ ] Phase 5: Analytics and Optimization

## Known Issues

As we are in the initial setup phase, there are no implementation issues yet. However, we have identified potential challenges:

### Technical Challenges

1. **Rate Limit Management**
   - Challenge: Effectively handling rate limits across multiple APIs
   - Potential Impact: Service disruption if not handled properly
   - Mitigation: Implementing robust rate limit tracking, queuing, and fallback mechanisms

2. **Authentication Security**
   - Challenge: Securely managing multiple API credentials
   - Potential Impact: Security vulnerabilities if not handled properly
   - Mitigation: Using environment variables and secure credential storage

3. **API Stability**
   - Challenge: Dealing with changes or disruptions in external APIs
   - Potential Impact: Service disruption or degraded functionality
   - Mitigation: Implementing robust error handling and fallback mechanisms

### Integration Challenges

1. **Twitter API Complexity**
   - Challenge: Navigating the complexity of Twitter API v2
   - Potential Impact: Difficulty implementing all required features
   - Mitigation: Using the twitter-api-v2 library and thorough testing

2. **Mastodon Instance Variations**
   - Challenge: Handling variations between different Mastodon instances
   - Potential Impact: Inconsistent behavior across instances
   - Mitigation: Testing with multiple instances and implementing adaptive logic

3. **AI Model Differences**
   - Challenge: Managing differences in AI model capabilities and outputs
   - Potential Impact: Inconsistent content quality
   - Mitigation: Implementing model-specific prompt engineering and output processing

## Next Milestones

1. **Complete Media Handling**
   - Target: Implement media upload for Twitter
   - Target: Implement media upload for LinkedIn
   - Target: Implement media upload for Mastodon

2. **Implement Analytics Collection**
   - Target: Implement engagement metrics retrieval for all platforms
   - Target: Create performance tracking system
   - Target: Develop analytics dashboard

3. **Enhance Research Capabilities**
   - Target: Create caching mechanism for research results
   - Target: Improve research aggregation with AI-powered insights
   - Target: Add support for more research sources