Skip to main content
Glama

create_workout

Build a custom Speediance workout template with ordered exercises, sets, reps, weights, and rest times for personalized training plans.

Instructions

Create a custom workout template. exercises is an ordered list of {"name": "..." or "group_id": N, "sets": [...], "rest_seconds": 60}. Sets by movement kind: reps -> {"reps": 10, "weight": 50}; timed -> {"seconds": 45}; Vita (level) -> {"seconds": 30, "level": 12}. Optional per set: "side" 1=left / 2=right (unilateral moves alternate automatically), "rest". Weights are in displayUnit. The template is read back after saving; verified:false means Speediance stored something different — tell the user. Never program a ⊘avoided movement unless asked by name. The reply carries the user's hardConstraints and legacyUnreviewed facts when there are any. Re-check these against the workout before telling the user it's done.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
exercisesYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and delivers: it discloses read-back verification, the meaning of verified:false, and that the reply carries hardConstraints and legacyUnreviewed facts requiring re-check. It omits permission/auth requirements and reversibility, but the post-save behavior disclosure is unusually rich.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the one-line purpose, then dense, purposeful detail on structure and post-conditions. Every clause adds information; the embedded JSON snippets and ⊘ symbol make it slightly cramped, but nothing is wasted.

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 tool with no annotations and no output schema, the description supplies the create contract, verification semantics, and key return fields. It is largely self-sufficient; only auth/permission context is missing, which is minor here.

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 coverage is 0%, so the description must compensate, and it does so thoroughly for `exercises`: object shape, set formats by movement kind (reps/timed/Vita), optional side/rest fields, and weight units. Only `name` is left unexplained, which is trivially inferable, keeping this just short of exemplary.

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

Purpose4/5

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

States a specific verb+resource: 'Create a custom workout template.' This clearly distinguishes it from read/list siblings, but it does not explicitly differentiate itself from update_workout or schedule_workout, leaving the create-vs-schedule boundary implicit.

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?

Provides some in-flight guidance ('Never program a ⊘avoided movement unless asked by name', verify before declaring done), but gives no explicit when-to-use/when-not guidance versus siblings like update_workout or schedule_workout. Usage context is implied rather than stated.

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