ble-hrm-server 0.2.0

A MCP server, which serve as a BLE heart rate monitoring to connect with a HRM device.

ble-hrm-server

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

Build Status

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 also run the server directly using uvx:

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

Code Coverage

Name	Stmts	Miss	Cover
src/hrm/__init__.py	0	0	100%
src/hrm/bt_client.py	139	5	96%
src/hrm/ts_db.py	41	1	98%
TOTAL	180	6	97%

Sponsor

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