[New MCP Server]: Social Sentiment Trading MCP Server

[FidelCoder]

🏷️ MCP Server Name

Social Sentiment Trading MCP Server

🏗️ MCP Category

Price Data/Analytics

📝 MCP Description

This MCP server aggregates social sentiment from multiple platforms (Reddit, Twitter/X, Farcaster, Discord, Telegram) and correlates it with on-chain data for trading decisions. It provides real-time sentiment analysis, early signal detection, and social momentum scoring for DeFi tokens.

Key capabilities:

• Multi-platform sentiment aggregation (Reddit, Twitter/X, Farcaster, Discord, Telegram)
• Real-time sentiment analysis with contextual explanations
• Early signal detection for emerging opportunities
• Social momentum scoring combining sentiment, volume, and velocity
• DeFi-specific sentiment categories (perpetuals, arbitrage, flashloans, lending, staking)
• Support for 14+ major DeFi tokens (ETH, BTC, ARB, UNI, AAVE, etc.)

Why it's needed:
Enables DeFi agents to make data-driven trading decisions based on social signals, detect early trends before they become mainstream, and avoid FOMO traps by analyzing sentiment across multiple platforms.

🌐 Data Source/API

Primary APIs:

• Reddit: Public JSON endpoints (no authentication required, free tier)
• Twitter/X API v2: Bearer token authentication (free tier available)
• Farcaster: Neynar API (free tier: 1,000 requests/day, cast search requires paid plan)
• Discord: Discord.js bot API (free, requires bot invitation to servers)
• Telegram: Telegram Bot API (free, requires bot added to groups/channels)
• Hugging Face: Sentiment analysis models (optional, free tier: 1,000 requests/day)

API details:

• Authentication: API keys for Twitter, Farcaster, Discord, Telegram (all free tiers available)
• Rate limits: Varies by platform, implementation includes caching to respect limits
• Documentation: All APIs are well-documented and widely used
• Reliability: High uptime across all platforms

🔧 MCP Tools to Implement

1. analyze-social-sentiment

   • Description: Analyze social sentiment for a token across Reddit, Twitter/X, and Farcaster. Returns sentiment score, confidence, trend, contextual explanations, key themes, actionable insights, and DeFi-specific categories.
   • Input: tokenSymbol (string, required), timeRange.hours (number, optional, 1-168, default: 24)
   • Output: JSON with sentiment score (-1 to 1), confidence (0 to 1), trend (bullish/bearish/neutral), sources breakdown, contextual analysis, and DeFi categories

2. social-momentum-score

   • Description: Calculate a combined social momentum score (0-100) based on sentiment, volume, and velocity across all platforms. Includes model explanations and action suggestions.
   • Input: tokenSymbol (string, required)
   • Output: JSON with overall score, breakdown by platform, velocity, volume, interpretation, and recommendations

3. detect-early-signals

   • Description: Detect early social signals (volume spikes, sentiment shifts) before they become mainstream. Useful for finding emerging opportunities.
   • Input: tokenSymbol (string, required), lookbackHours (number, optional, 1-168, default: 24)
   • Output: JSON with signal type, strength, description, historical comparison, and relevant posts

4. list-supported-tokens

   • Description: List all supported tokens with their names and search configurations.
   • Input: None
   • Output: JSON with list of supported tokens, categories (Layer 1, Layer 2, DeFi, stablecoins), and metadata

🤖 Agent Integration Examples

Example: Trading Agent

• Agent uses analyze-social-sentiment to check sentiment before executing swaps
• Uses social-momentum-score to assess market momentum and timing
• Sets up detect-early-signals to identify emerging opportunities before price moves
• Uses list-supported-tokens to discover available tokens for analysis

Example: Risk Management Agent

• Monitors sentiment across platforms using analyze-social-sentiment
• Uses DeFi-specific categories to assess risk in perpetuals, arbitrage, flashloans
• Detects early signals to avoid FOMO traps
• Correlates social momentum with on-chain data for comprehensive risk assessment

🔐 Authentication Requirements

API key required (free tier available)

⚙️ Configuration Options

Required configuration:

• TWITTER_BEARER_TOKEN: Twitter/X API bearer token (optional, for Twitter integration)
• NEYNAR_API_KEY: Neynar API key for Farcaster (optional, for Farcaster integration)
• DISCORD_BOT_TOKEN: Discord bot token (optional, for Discord integration)
• DISCORD_CHANNEL_IDS: Comma-separated Discord channel IDs (optional, for Discord integration)
• TELEGRAM_BOT_TOKEN: Telegram bot token (optional, for Telegram integration)
• TELEGRAM_CHAT_IDS: Comma-separated Telegram chat IDs (optional, for Telegram integration)

Optional configuration:

• HUGGING_FACE_API_KEY: Hugging Face API key (improves sentiment analysis accuracy)
• REDDIT_USER_AGENT: Custom user agent string for Reddit API

Environment setup:

• Copy .env.example to .env and add API keys
• For Discord: Create bot at https://discord.com/developers/applications, enable Message Content Intent, invite to servers
• For Telegram: Create bot via @Botfather, add to groups/channels, get chat IDs
• All platforms support free tiers

🧪 Testing Strategy

Unit tests:

• Test sentiment aggregation logic
• Test token mapping and search term generation
• Test caching mechanism
• Test error handling for API failures

Integration tests:

• Test each tool with real API calls (using mocks for rate limiting)
• Test multi-platform aggregation
• Test early signal detection heuristics
• Test momentum score calculation

Performance tests:

• Test caching effectiveness
• Test rate limit handling
• Test concurrent requests

✅ Pre-submission Checklist

• I have searched existing issues to avoid duplicates
• I have clearly described the MCP server's functionality
• I have identified the data source and API requirements
• I will wait for the Vibekit team to approve this issue before continuing to implementation