amazfit-mcp

summarize_workouts

Summarize training totals by day, week, month, year, or all time. Get count, hours, km, calories, elevation, load, and average HR with optional sport filtering.

Instructions

Training totals per period: count, hours, km, calories, elevation, training load, and duration-weighted avg HR.

group_by: "day", "week" (ISO week), "month", "year", or "all".
by_sport: split each period per sport (default true).
from_date/to_date: optional ISO YYYY-MM-DD bounds; defaults to the last
12 weeks. sport: optional substring filter.
Multisport parents are skipped; their individual legs are counted.

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": "summarize_workouts"
  }
}

Input Schema

Name	Required	Default
`sport`	No
`to_date`	No
`by_sport`	No
`group_by`	No	week
`from_date`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 26, 2026

TDQS

A4.6/5.0

Behavior4/5

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

With no annotations, the description carries the full behavioral burden. It discloses a non-obvious aggregation rule ('Multisport parents are skipped; their individual legs are counted') and states the default 12-week date window. It could add a note about read-only guarantees or empty-result behavior, but the disclosed traits are valuable and non-obvious.

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 tightly structured: a summary line, one line per parameter, and one final behavioral note. No filler or redundant phrasing. It front-loads the core purpose before parameter details.

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

Completeness5/5

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

The tool has 5 optional parameters and an output schema, so the description need not explain return values. It covers all parameters, defaults, date formats, the default range, and a critical edge case (multisport handling). Nothing an agent needs to call this tool correctly is missing.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must fully compensate. It does: every parameter is explained with its allowed values (group_by options), default (by_sport=true), format (ISO dates), and meaning (sport substring filter). This is exemplary compensation for an empty schema.

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

Purpose5/5

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

The description opens with a precise verb and resource ('Training totals per period') and enumerates the exact metrics computed (count, hours, km, calories, etc.), making it unmistakably an aggregation tool. This clearly separates it from sibling tools that list workouts or return details/tracks for individual activities.

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

Usage Guidelines4/5

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

The description provides very clear functional context: it summarizes workouts by period with grouping and sport-splitting options. It does not explicitly name alternatives or state when not to use it, but the aggregation purpose is so unambiguous that an agent can infer the right use case.

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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