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Renpho MCP Server

Renpho MCP Server

by StartupBros-com
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

get_body_composition

Retrieve detailed body composition analysis including weight, BMI, body fat percentage, muscle mass, and health classifications from Renpho smart scales for health monitoring and trend tracking.

Instructions

Get detailed body composition analysis with health classifications

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_body_composition"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.1/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'detailed body composition analysis' and 'health classifications,' which imply a read operation, but fails to specify aspects like data freshness, authentication needs, rate limits, or error conditions. This leaves significant gaps for a tool that likely involves health data.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose ('Get detailed body composition analysis') and adds value with 'health classifications.' There is no wasted verbiage, making it highly concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of health data tools and the lack of annotations and output schema, the description is insufficient. It doesn't explain what 'detailed analysis' entails, the format of health classifications, or potential limitations, leaving the agent under-informed for proper tool invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately adds context about what the tool retrieves (body composition analysis with health classifications) without redundant parameter details, aligning with the baseline for zero-parameter tools.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose as 'Get detailed body composition analysis with health classifications,' which specifies the action (get) and resource (body composition analysis). It distinguishes itself from siblings like 'get_weight_trend' by focusing on composition rather than weight trends, though it doesn't explicitly differentiate from 'get_measurements' which might overlap.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'get_measurements' or 'get_latest_measurement.' It lacks explicit instructions on context, prerequisites, or exclusions, leaving the agent to infer usage based on tool names alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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