SocialPulse AI

基于TensorFlow.js构建的AI驱动社交媒体分析与自动化工具。该服务器提供一套机器学习工具，包括情感分析、有害内容检测、互动预测和内容聚类，用于分析YouTube、Instagram和Twitter等多个平台上的互动。它在本地运行，以确保数据隐私和低延迟处理。功能：跨平台支持、本地ML模型执行、支持x402变现。

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TensorFlow.js Social Media MCP Server

MCP Server

A comprehensive suite of machine learning tools for analyzing and automating interactions across multiple social media platforms, built on TensorFlow.js and powered by the Model Context Protocol (MCP).

This server performs ML-based analytics locally (e.g., sentiment analysis, toxicity detection, engagement prediction, content clustering) without relying on external cloud-based AI services, ensuring data privacy and low-latency processing.

Key Features

  • Cross-Platform Support: Tools for YouTube, Instagram, TikTok, Twitter, Facebook, Discord, Twitch, Reddit, LinkedIn, Threads, Bluesky, Mastodon, GitHub, Spotify, and Pinterest.
  • Local Machine Learning: Powered by TensorFlow.js (MobileNet, COCO-SSD, Universal Sentence Encoder, etc.).
  • Monetization Ready: Integrated x402 protocol for charging per tool call via Solana (devnet/mainnet).
  • Extensible: Easily add new tools via lib/ modules and tool-registry.js.
  • Ready for Agents: Full OpenAPI 3.0 specification for seamless integration with AI agents.

Setup Requirements

Prerequisites

Configuration

Copy .env.example to .env and configure the following:

VariableDescriptionDefault
PAY_TO_ADDRESSSolana wallet address to receive x402 payments.REQUIRED
FACILITATOR_URLx402 facilitator URL.https://x402.org/facilitator
X402_PRICE_USDPrice per tool call in USD.0.005
X402_NETWORKPayment network (devnet or mainnet).devnet
PUBLIC_ORIGINPublic URL for the server (needed for discovery).http://localhost:6350

Installation

npm install

Running the Server

Start the MCP Server

npm run mcp

Start all services (MCP, REST, Worker)

npm run start:all

Start the TUI

npm run tui

API Documentation

The server exposes an OpenAPI 3.0 specification at http://localhost:6350/openapi.json.

See readme.txt in the root for a summary of available endpoints.

Contributing

  1. Fork the repository.
  2. Create your feature branch.
  3. Commit your changes.
  4. Push to the branch.
  5. Open a Pull Request.

License

MIT