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elkno

GymTimer MCP Server

by elkno

Get Workout History

get_workout_history

Fetch completed workouts from the last N days, sorted by date descending. Includes every exercise, set, and difficulty rating to assess intensity and volume.

Instructions

Returns completed workout sessions (with every exercise and set) from the last N days, newest first. Use this to see what the user actually did recently. Each session includes difficultyRating (1-5, how hard the user said the workout felt right after finishing it, 5 being extremely hard) when available - use it alongside volume/reps trends to judge whether intensity or volume should change.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoHow many days back to look, e.g. 7, 14, 30

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.0.0

TDQS

A4.2/5.0
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 that only completed sessions are returned, that every exercise and set is included, that results are newest first, and that difficultyRating is a 1-5 user-reported value available only sometimes. This is meaningful behavioral context beyond the schema, though it stops short of describing the full return envelope or pagination.

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 two sentences, starts with the core result immediately, and each clause earns its place: scope, ordering, use case, and rating semantics. No filler or redundant restatement.

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?

For a simple single-parameter read tool, the description is largely complete: it covers what is returned, the ordering, the time window, and the meaning and use of difficultyRating. Since there is no output schema, a bit more detail about the session object structure could help, but the low complexity and full parameter schema make this a minor gap.

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

Parameters3/5

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

Schema coverage is 100%, with the days parameter already described as 'How many days back to look.' The description adds the interpretation 'from the last N days' and 'newest first,' which slightly enriches the semantics, but the schema already carries most of the parameter meaning. Baseline 3 is appropriate.

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 names a specific verb and resource: 'Returns completed workout sessions (with every exercise and set) from the last N days, newest first.' It clearly differs from siblings like get_exercise_history or analyze_training_volume by emphasizing completed sessions with full set detail and recency ordering, so an agent can distinguish it without opening schemas.

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 gives an explicit use case: 'Use this to see what the user actually did recently.' It also suggests combining it with volume/reps trends to judge intensity or volume changes, providing practical context. It does not name alternatives or explicitly say when not to use it, but the primary usage is clear.

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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