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fjuliofontes

amazfit-mcp

by fjuliofontes
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_workout_track

Fetch per-sample time series for a workout—GPS, heart rate, speed, distance, power, cadence—downsampled to a set number of points. Specify fields to focus on relevant metrics.

Instructions

Decoded per-sample time series for one workout, downsampled to at most max_points evenly spaced points (t_s = seconds from start).

Possible fields (only those recorded are returned): lat, lon, altitude_m,
hr, speed_kmh, distance_m, cadence_spm, stride_cm, power_w,
stroke_rate_spm. Pass `fields` to limit output, e.g. ["hr", "speed_kmh"].

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fieldsNo
trackidYes
max_pointsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the full behavioral burdenasi. It discloses downsampling to at most max_points evenly spaced points, the meaning of t_s, that only recorded fields are returned, and that fields limits output. It does not cover errors or auth, but for a read-oriented tool this is strong transparency.

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?

Three tight sentences front-load the core behavior, then enumerate possible fields and filtering. There is no filler, and every sentence adds usable information.

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

Completeness4/5

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

The description covers the essential behavior, field semantics, and sampling rules. It lacks an explicit return-shape statement, which matters since no output schema exists, but the 'per-sample time series' framing plus the field list gives enough for an agent to invoke and interpret the response correctly.

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?

Schema description coverage is 0%, so the description must compensate. It clearly explains fields with allowed values and an example, and max_points with its downsampling behavior. The trackid parameter is not explicitly described, though its role is reasonably inferable from the tool name and 'one workout'.

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 states a specific verb and resource: 'Decoded per-sample time series for one workout'. This clearly distinguishes it from sibling tools like summarize_workouts and get_workout_detail, which by their names imply aggregate or summary data rather than raw time series.

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

Usage Guidelines3/5

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

The description implies usage context by emphasizing per-sample time series and optional field filtering, but it does not explicitly state when to prefer this tool over get_workout_detail or list_workouts. There are no exclusions or alternative routing, so the guidance is inferred rather than stated.

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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