Skip to main content
Glama

set_preferences

Update your training preferences by changing only the fields you specify, including goal, training days, session length, owned and unusable equipment, and weight anchors.

Instructions

Update structured preferences (only the fields given change). load_anchors maps group_id -> a known working weight in displayUnit and is MERGED into the saved anchors (other anchors are kept); a weight of null or 0 removes that anchor. owned_equipment is a list of accessory names from list_accessories (it replaces the saved list). unusable_equipment names gear the user OWNS but cannot use (an injury, a bench they can't lie on): movements needing it are hidden from list_exercises and must never be planned. Free-text facts go in remember_fact instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
goalNo
load_anchorsNo
training_daysNo
owned_equipmentNo
session_minutesNo
unusable_equipmentNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries the full load and largely delivers: it discloses merge semantics for load_anchors (other anchors kept), removal via null/0, replacement semantics for owned_equipment, and the downstream effect that unusable_equipment hides movements. What it omits is auth/permission needs and the merge/replace behavior for the remaining fields (goal, training_days, session_minutes).

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 general update semantics before per-field detail, and dense with no filler. It is long but every sentence conveys a distinct rule; a semicolon-separated field-per-clause layout keeps it scannable.

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

Completeness3/5

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

For a mutation tool with no annotations, no output schema, and 0% schema coverage, the description does well on semantics and downstream effects but leaves three parameters and any return/permission behavior unstated. It is adequate but not fully complete for a 6-param mutator.

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 description coverage is 0%, so the description must compensate, and it richly documents 3 of 6 parameters (load_anchors, owned_equipment, unusable_equipment) including value format (group_id -> weight in displayUnit). However goal, training_days, and session_minutes remain undocumented in both schema and description, leaving half the surface unexplained.

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?

States a specific verb+resource ("Update structured preferences") and immediately scopes it with "only the fields given change". It also names and rules out the sibling remember_fact, so an agent can distinguish this from the free-text path without opening either schema.

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?

Gives clear routing: free-text facts belong in remember_fact, and equipment names should come from list_accessories. It lacks an explicit overall when-to-use trigger (e.g. vs get_preferences), but the alternative-tool guidance is unusually concrete.

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