What does `calculate_bmi` return?
It returns a number (`bmi_index`) representing the Body Mass Index calculated from the input weight and height.
@oscarrenalias
基本情報
概要はまだありません
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"mcp-bmi": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/oscarrenalias/mcp-server-bmi:latest"
]
}
}
}ツール一覧はまだありません
この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。
ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。
MCP BMI is a simple MCP server that exposes two tools: one to calculate a person’s BMI from their weight and height, and another to return the corresponding BMI category. It is built on Python with FastMCP, packaged as a Docker container, and published to the GitHub Container Registry. It is intended for AI assistants or applications that need on‑demand BMI computation.
The server can be used in two ways. In Visual Studio Code, add a Docker run command to your settings.json. Locally, run uv sync && uv run bmi_server.py or build and run the Docker image with docker build -t mcp-bmi . followed by docker run --rm -it mcp-bmi. The server provides two tools: calculate_bmi (requires weight and height as float inputs) and bmi_category (requires bmi as a float input).
calculate_bmi tool: returns a numeric BMI index from weight and height.bmi_category tool: returns a string category based on the BMI value.test_bmi.py) that can be run with uv run test_bmi.py.It returns a number (`bmi_index`) representing the Body Mass Index calculated from the input weight and height.
It returns a string (`bmi_category`) with one of seven predefined categories: Underweight, Healthy Weight, Overweight, Obesity, Class 1 Obesity, Class 2 Obesity, or Class 3 Severe obesity.
Add the following to your `settings.json`: ```json { "servers": { "command": "docker", "args": [ "run", "-i", "--rm", "ghcr.io/oscarrenalias/mcp-server-bmi:latest" ] } } } ```
Yes. Run `uv sync` then `uv run bmi_server.py` to start the server directly on your machine. Python and `uv` are required.
The server is built with Python and FastMCP. For local use, you need Python and `uv`. For Docker usage, you need Docker. The server does not require any external API keys or network access.
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