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Reduces AI Agent token usage by 40% via three-stage SOP workflow.
About
Reduces AI Agent token usage by 40% via three-stage SOP workflow.
Remote endpoints: streamable-http: https://mcp.huangting.ai/mcp
Security Report
Well-structured MCP server implementing a token optimization protocol. Code quality is good with proper error handling, permissions match the stated purpose, and no significant security vulnerabilities were found. Minor concerns include basic input validation on Redis keys and a potential false positive pattern identified by the scanner. Supply chain analysis found 6 known vulnerabilities in dependencies (0 critical, 5 high severity).
4 files analyzed · 8 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
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How to Install & Connect
Available as Local & Remote
This plugin can run on your machine or connect to a hosted endpoint. during install.
Documentation
View on GitHubFrom the project's GitHub README.
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:
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:
{
"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"
}
}
}'
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
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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