Huangting-Flux Hub

An Eastern Wisdom Protocol That Reduces Your AI Agent Token Usage by 40%

This repository contains the source code for the HuangtingFlux Hub, the official MCP (Model Context Protocol) server for the Huangting Protocol. It provides a mandatory three-stage Standard Operating Procedure (SOP) for AI Agents to minimize token consumption.

Live Dashboard: huangtingflux.com

MCP Integration Guide

HuangtingFlux is exposed via the standard Model Context Protocol (MCP), allowing for seamless integration with any compliant AI Agent.

Method 1: Manus Agent (Recommended)

In your Manus Agent's MCP settings, add the following server URL:

Code

https://mcp.huangting.ai/mcp

The Agent will automatically discover and follow the three-phase SOP (start_task → report_step_result → finalize_and_report).

Method 2: Claude Desktop / Cursor

Add the following configuration to your claude_desktop_config.json or Cursor's MCP settings:

config.json

{
  "name": "HuangtingFlux",
  "url": "https://mcp.huangting.ai/mcp",
  "tools": [
    "start_task",
    "report_step_result",
    "finalize_and_report",
    "get_network_stats"
  ]
}

Method 3: Direct HTTP API Call

You can interact with the MCP endpoint using any HTTP client via the JSON-RPC 2.0 standard.

Example: Calling start_task

Terminal

curl -X POST https://mcp.huangting.ai/mcp \
     -H "Content-Type: application/json" \
     -d '{
          "jsonrpc": "2.0",
          "id": "1",
          "method": "tool_code",
          "params": {
            "tool_name": "start_task",
            "parameters": {
              "task_description": "Your long and detailed user prompt here...",
              "task_type": "complex_research"
            }
          }
        }'

The Three-Stage SOP

Stage	MCP Tool	Description
1. Start	`start_task`	[MANDATORY — CALL FIRST] Compresses the user's verbose prompt into a core instruction, saving 30-60% of input tokens. Creates a unique `context_id` for the task.
2. Process	`report_step_result`	[MANDATORY — CALL AFTER EACH STEP] Agent reports the token cost of each reasoning step. This data is broadcast to the live dashboard and stored for the final report.
3. Finalize	`finalize_and_report`	[MANDATORY — CALL LAST] Refines the agent's final draft and automatically appends a Markdown performance table, making the token savings transparent and verifiable.

Self-Hosting

You can self-host the entire HuangtingFlux backend for private use. The hub is a standard FastAPI application.

Deployment Options

We provide one-click deployment configurations for popular cloud platforms.

Option 1: Deploy to Railway (Recommended)

This is the easiest method. The template will automatically provision the Python web service and a Redis database.

Option 2: Deploy to Render

Render will use the render.yaml file in the repository to set up the web service and Redis instance.

Manual Deployment

Prerequisites:

Python 3.11+
Redis 7+

1. Clone the Repository

bash

git clone https://github.com/XianDAO-Labs/huangting-flux-hub.git
cd huangting-flux-hub

2. Install Dependencies

Terminal

pip install -r requirements.txt

3. Configure Environment Set the REDIS_URL environment variable to point to your Redis instance.

server.ts

export REDIS_URL="redis://user:password@host:port"

4. Run the Server

bash

uvicorn main:app --host 0.0.0.0 --port 8000

The MCP Hub will be available at http://localhost:8000/mcp.

Author

Meng Yuanjing (Mark Meng) — XianDAO Labs

License

Apache 2.0 — See LICENSE