This server enforces a mandatory three-stage workflow that compresses user prompts before sending them to your LLM, tracks token usage per reasoning step, and appends performance metrics to final outputs. You call start_task to get a condensed instruction and context_id, report_step_result after each operation to log costs, then finalize_and_report to append a markdown table showing savings. It broadcasts stats to a live dashboard at huangtingflux.com and claims 40% token reduction on complex tasks. Built on FastAPI with Redis backing, it exposes these three tools plus get_network_stats over MCP's streamable HTTP transport. Reach for it if you want enforced token accounting baked into your agent loop rather than bolted on afterward.
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
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_task → report_step_result → finalize_and_report).
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"
]
}
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"
}
}
}'
| 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. |
You can self-host the entire HuangtingFlux backend for private use. The hub is a standard FastAPI application.
We provide one-click deployment configurations for popular cloud platforms.
This is the easiest method. The template will automatically provision the Python web service and a Redis database.
Render will use the render.yaml file in the repository to set up the web service and Redis instance.
Prerequisites:
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.
Meng Yuanjing (Mark Meng) — XianDAO Labs
Apache 2.0 — See LICENSE
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