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HuangtingFlux Hub

by XianDAO-Labs

Huangting-Flux Hub

Protocol MCP FastAPI Python

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.

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

https://mcp.huangting.ai/mcp

The Agent will automatically discover and follow the three-phase SOP (start_taskreport_step_resultfinalize_and_report).

Method 2: Claude Desktop / Cursor

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

{
  "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

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"
            }
          }
        }'

Related MCP server: toon-parse-mcp

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.

Deploy to Railway

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

Option 2: Deploy to Render

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

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

2. Install Dependencies

pip install -r requirements.txt

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

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

4. Run the Server

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

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