mcpField
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Ble Hrm Server

Ble Hrm Server

Local
github.com/ThinkInAI-HackathonHealth2Updated 71w ago

About Ble Hrm Server

ble-hrm-server is a Python MCP server for connecting to Bluetooth heart rate monitor devices. It provides heart-rate monitoring, measurements, summaries, and chart generation for applications that use MCP clients.

Key features

  1. 01Discover devices with the HRM profile
  2. 02Monitor heart rate for a chosen duration
  3. 03Get an average from recent readings
  4. 04Evaluate maximum heart rate
  5. 05Return time-bucketed readings and charts

Use cases

  1. 01Monitor heart rate during exercise
  2. 02Check a recent average heart rate
  3. 03Review peak heart rate after activity
  4. 04Generate a chart of recent readings

MCP support

It exposes an HRM device discovery resource and tools for monitoring, retrieving, evaluating, and charting heart-rate readings to MCP clients.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"ble-hrm-server": {
"command": "uvx",
"args": [
"ble-hrm-server"
]
}
}
}
Run commanduvx ble-hrm-server

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
pypible-hrm-server

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

ble-hrm-server

A MCP server, which serve as a BLE Heart Rate Monitoring to connect with a HRM device.

Build Status

CI

Installation

We recommend using a virtual environment and uv to manage dependencies.

This project uses pyproject.toml for dependency management.

# Create and activate a virtual environment
python3 -m venv .venv
source .venv/bin/activate  # On Windows use `.venv\Scripts\activate`

# Install uv if not already installed
pip install uv

# Install dependencies
uv sync

Environment Configuration

This project supports configuration via a .env file. You can use the provided .env.sample as a template.

cp .env.sample .env

The following environment variables are used:

  • QINIU_ACCESS_KEY: Your Qiniu access key.
  • QINIU_SECRET_KEY: Your Qiniu secret key.
  • QINIU_BUCKET_NAME: The name of the Qiniu bucket.
  • QINIU_BUCKET_DOMAIN: The domain associated with the Qiniu bucket.

Usage

Run the server

uv run src/hrm/server.py

After installing from PyPI, you can run the server directly using:

uvx ble-hrm-server

Run the tests

uv run pytest

Run the tests with coverage

uv run coverage run -m pytest
uv run coverage html

MCP Definition

Resources

Bluetooth HRM is based on Bluetooth protocol, we should use bleak to discover the device and connect to it.

  • Discover Bluetooth Device & Filter by HRM profile (Heart Rate Service 0x180D)
  • resource: discover://hrm

Tools

  • Tool: Monitoring Heart Rate monitoring_heart_rate

    • Summary: Start monitoring the heart rate of the device for the given duration, default duration is 30 minutes (1800 sec). The monitoring will be done in the background.
    • Inputs:
      • device_id: str, the device UUID to monitor
      • duration: int, the duration to monitor, default is 1800 seconds (30 minutes)
    • Outputs: None
  • Tool: Get Heart Rate get_heart_rate

    • Summary: Get the current HR, use last 10 sec and return the average of HR
    • Inputs: None (assumes baseline reading)
    • Output: Current HR (int), e.g. {"avg_hr": 60}
  • Tool: Evaluate Active Heart Rate evaluate_active_heart_rate

    • Summary: Evaluate the maximum heart of the last 60 seconds, the observed maximum during this exercise is recorded as the active heart rate.
    • Inputs: None (assumes baseline reading)
    • Outputs: Max HR: int, e.g. {"max_hr": 100}
  • Tool: Get Heart Rate Bucket get_heart_rate_bucket

    • Summary: Get the heart rate bucket of the last 10 seconds, the bucket size is 1 second.
    • Inputs:
      • since_from: float, the start time of the monitoring, default is 10 seconds ago
      • bucket_size: float, the size of the bucket, default is 1 second
    • Outputs: Heart Rate Bucket: list[dict], e.g. [{"time": 1715904000, "value": 60}, {"time": 1715904001, "value": 61}]
  • Tool: Build Heart Rate Chart build_heart_rate_chart

    • Summary: Build the heart rate chart of the last 600 seconds, the bucket is a dynamic size, the default size is 1 second. The max bucket count is 60, if the bucket count is more than 60, the bucket size will be increased to duration / 60 seconds.
    • Inputs:
      • since_from: float, the start time of the monitoring, default is 600 seconds ago
    • Outputs: Heart Rate Chart PNG URL: str, e.g. https://example.com/chart.png

MCP Settings

Here is the MCP settings for the server:

{
  "mcpServers": {
    "blue-hrm": {
      "command": "uvx",
      "args": [
        "ble-hrm-server"
      ]
    }
  }
}

Here is a sample prompt for the LLM tested by DeepSeek V3.1:

你是一位健康专家,通过心率来检测测试者的心脏健康程度,现在测试者携带了蓝牙的心率带,

以下为检测步骤
- 你通过发现来获取的设备ID,通过rssi获取最接近的设备,再以此设备开始监控。
- 获取当前测试者的静息心率。
- 当测试结束后,提示测试者开始中高强度运动,比如波比跳,当测试者完成运动后,回复后,立刻测试其最高运动心率。
- 之后提示测试者计时休息2分钟,得到确认后,再次获取当前心率,并与最高心率的差额作为恢复心率。 

最后通过恢复心率的值和以下列表评估测试者的心脏健康程度,并用健康专家的口吻给测试者一些关于心脏健康方面的建议。
------
- <22: 身体年龄略大于 真实年龄
- 22~52: 身体年龄= 真实年龄 
- 53~58: 身体年龄略小于真实年龄 
- 59~65: 身体年龄比真实年龄小 
- >=66: 身体年龄比真实年龄小很多

Code Coverage

NameStmtsMissCover
src/hrm/__init__.py00100%
src/hrm/bt_client.py139596%
src/hrm/ts_db.py41198%
TOTAL180697%

Sponsor

Special thanks for Qiniu Cloud for providing the storage service. Qiniu also provides LLM inference services.

Qiniu Logo

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