Huangting Protocol MCP server: concept queries, optimization strategies, result reporting.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β or use 1-click editor setup below.
π‘ Paste into ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows)
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:
The Agent will automatically discover and follow the three-phase SOP (start_task β report_step_result β finalize_and_report).
Add the following configuration to your claude_desktop_config.json or Cursor's MCP settings:
You can interact with the MCP endpoint using any HTTP client via the JSON-RPC 2.0 standard.
Example: Calling start_task
| Stage | MCP Tool | Description |
|---|---|---|
| 1. Start | start_task | [MANDATORY β 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 β 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 β 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
2. Install Dependencies
3. Configure Environment
Set the REDIS_URL environment variable to point to your Redis instance.
4. Run the Server
The MCP Hub will be available at http://localhost:8000/mcp.
Meng Yuanjing (Mark Meng) β XianDAO Labs
Apache 2.0 β See LICENSE
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