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update_sets

Destructive

Correct sets already logged, by set id (log_workout returns them). Relabel the exercise, fix weight/reps/rpe, mark completed, add notes, or delete. Selection and change are separate, so a subset can be relabelled. Adding a set is log_workout, not this.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rpeNo
repsNo
notesNo
deleteNo
weightNo
set_idsYes
exerciseNoCatalog name, ext_id, or the user's own words for a custom movement.
completedNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
deletedNo
missingNo
updatedNo

TDQS

A4.6/5.0
Behavior4/5

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

The description adds behavioral nuance beyond annotations by explaining the separate selection/change behavior, enabling partial updates like relabeling a subset. It also mentions the delete option, consistent with destructiveHint=true, and clarifies that existing logged sets are modified rather than created.

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 three concise sentences, front-loaded with the core purpose, and every sentence earns its place. It avoids fluff while covering purpose, usage, and the key distinction from log_workout.

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?

The description is fairly complete for an 8-parameter destructive tool, covering the main operations and the source of set IDs. It does not explicitly mention irreversibility of delete, but the destructiveHint annotation and the word 'delete' cover that. The presence of an output schema also reduces the need to describe return values.

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?

With only 13% schema description coverage, the description compensates by explaining the key parameter set_ids (from log_workout) and mapping several fields to operations: relabel exercise, fix weight/reps/rpe, mark completed, add notes, delete. It does not detail every parameter, but it provides sufficient semantic context beyond the bare schema.

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 clearly states the tool corrects already-logged sets by set ID, with a specific verb ('Correct') and resource ('sets'). It lists the specific operations (relabel exercise, fix weight/reps/rpe, mark completed, add notes, delete) and distinguishes itself from log_workout for adding sets.

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 explicitly says 'Adding a set is log_workout, not this,' providing a clear when-not-to-use condition. It also gives context that set IDs come from log_workout, and explains that selection and change are separate, which guides partial updates.

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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TDQS

A3.7/5.0
Disambiguation4/5

Most tools have clearly distinct purposes (meals, workouts, routines, body metrics, etc.). A few pairs like get_progress and get_muscle_recovery overlap in data but differ in usage, and the AI tools are separated by input type. Overall, an agent can reliably select the right tool.

Naming Consistency4/5

Tool names predominantly follow a verb_noun snake_case pattern (get_, log_, update_, delete_, search_, list_). The ai_* prefix is consistent but includes noun-like names (ai_meal_plan, ai_photo_macros) that deviate slightly from verb-first convention. Still predictable and readable.

Tool Count3/5

With 36 tools, the server exceeds the typical 'heavy' threshold, but the scope is broad covering meals, workouts, routines, metrics, AI features, and data sync. Each tool serves a distinct capability, so the count is justified though on the higher end.

Completeness4/5

The server provides solid lifecycle coverage for core resources: meals (create/read/update/delete), workouts (log/get/delete, set updates), body metrics (get/log with profile upsert), routines (list/get/instantiate), and exercise lookup (search/resolve/list). Minor gaps exist for updating/deleting cardio and water entries, but they are not critical.

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