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Packi1992

CalisthenicsCompanion-MCP

by Packi1992

Get Workout History

get_history
Read-only

Fetch training sessions and set-logs within a date range, sorted newest-first, with optional exercise filter. Use for detailed inspection of specific workouts; results can be saved to a file for large data sets.

Instructions

Return sessions and set-logs within a date range (YYYY-MM-DD), newest-first, for a TARGETED detail lookup — a specific window, a specific exercise. For the athlete's overall standing, call get_training_state first; use this tool once you need the underlying raw sessions and set-logs. Optionally filter by exercise UUID. Results are capped by limit (default 200, max 500). Set outputFile: true to write the full result to a local file instead of returning it inline — the tool result then reports only the file path, byte size and item count.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toYes
fromYes
limitNo
exerciseNo
outputFileNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.7/5.0
Behavior4/5

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

The only annotation is readOnlyHint=true, so the description carries limited safety burden; it adds meaningful behavioral details beyond that: newest-first ordering, result cap behavior via limit, and the outputFile side effect of returning only path/byte size/item count. This goes well beyond what the annotation alone conveys, without contradicting it.

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 dense and front-loaded, with each sentence contributing a distinct fact: return scope, ordering, when-to-use guidance, optional filter, limit cap, and outputFile behavior. It is longer than the minimum but every clause earns its place.

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?

Given five parameters, no output schema, and a read-only annotation, the description covers the essential call requirements: required date range format, optional filter, limit behavior, ordering, and outputFile result reporting. It does not describe the shape of the inline session/set-log return, but the tool is still callable without that detail.

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 compensate, and it does: from/to are explained as a date range with YYYY-MM-DD format, exercise as an optional UUID filter, limit with default/maximum cap, and outputFile with its local-file writing behavior. Every parameter is given actionable meaning beyond its bare schema definition.

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 uses a specific verb ('Return') with a precise resource ('sessions and set-logs') scoped to a date range and optional exercise filter. It also differentiates itself from get_training_state by framing this as the targeted raw-detail lookup, so an agent can distinguish it from siblings.

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

Usage Guidelines5/5

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

It explicitly says to call get_training_state first for the athlete's overall standing and to use this tool when underlying raw sessions and set-logs are needed. That is direct when-to-use guidance with a named alternative.

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