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Packi1992

CalisthenicsCompanion-MCP

by Packi1992
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TypeScript
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Get Training State

get_training_state
Read-only

Retrieve a full athlete training snapshot in one call: current/longest streaks, session frequency, muscle balance, plan adherence, recent exercises, and personal records.

Instructions

The standard entry point for "how is the athlete doing" — a query-time-computed overview, rather than raw session-by-session history. In one call: the current and longest training streak in weeks plus session-frequency counts (consistency); the same radar-chart muscle balance data as the app's Analytics dashboard in all three app windows (muscleBalance); a plan-adherence short form — counts, ratio, and an honesty explanation about what removedOccurrenceCount does and does not mean (adherence); the most recently trained exercises with their direction labels (recentExercises); all-time personal records (personalRecords); and a short, deterministic narrative sentence built from these already-computed numbers, never a second model call. This tool stays narrow on purpose: it carries no point series and no pair lists. For per-exercise or per-format progress series, call get_progress. For the full adherence pair/missed lists, call get_adherence. For raw session history, call get_history. All calendar-day mapping binds to the athlete's own synchronized time zone, never the coach's own clock.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.9/5.0
Behavior5/5

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

With readOnlyHint already present, the description adds meaningful behavior beyond the annotation: the tool computes results at query time, produces a deterministic narrative without a second model call, and binds calendar days to the athlete's timezone rather than the coach's. It also explains the semantics and limits of removedOccurrenceCount, which is valuable 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?

The description is dense but every clause earns its place: it front-loads the core purpose, enumerates the returned content areas, states exclusions, names alternatives, and clarifies timezone behavior. The length is justified because there is no output schema to carry the return-value 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?

Given no output schema and zero parameters, the description is remarkably complete: it enumerates the main result categories, names the sibling tools for adjacent cases, specifies the read-only computed nature, and clarifies the timezone binding. An agent has everything needed to select and invoke this tool 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?

The tool has zero parameters and an empty input schema, so there are no parameter semantics to document. The description still indirectly clarifies that the tool takes no arguments and needs no configuration, which is sufficient for this vacuous case.

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 clear, specific purpose: it is the standard entry point for an athlete overview, computed at query time rather than raw history. It also distinguishes itself from siblings by naming exactly what it does not carry (point series, pair lists) and which sibling tools cover those cases.

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?

The description gives explicit routing guidance: use get_progress for per-exercise/format progress series, get_adherence for full adherence pair/missed lists, and get_history for raw session history. This leaves no ambiguity about when to choose this tool over its siblings.

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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