Log a workout with exercise sets (strength or cardio unified). Requires an app user context.
| Name | Required | Description | Default |
|---|---|---|---|
| date | Yes | ||
| sets | No | ||
| title | No | ||
| primary_sport | No | ||
| idempotency_key | No |
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | ||
| date | No | ||
| title | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With annotations all false, the description must carry the behavioral disclosure burden. It adds a meaningful prerequisite (requires an app user context) and the verb 'log' implies an additive write, but it does not disclose idempotency behavior, potential side effects, or response structure. This is limited but not misleading.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences with no filler. Each sentence adds a distinct point: what the tool does and a prerequisite. It is efficiently structured and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a fairly complex tool with 5 parameters, a nested sets array, and an idempotency_key, yet the description is very sparse. It fails to explain the structure of sets, the meaning of primary_sport, or the idempotency mechanism, making it incomplete for an agent to use correctly without additional inference.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description is the only source of parameter meaning. It mentions 'exercise sets' and that they can be strength or cardio unified, but ignores key parameters like date, title, primary_sport, and idempotency_key. This leaves most parameters unexplained.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Log' and the resource 'workout', and specifies 'with exercise sets (strength or cardio unified)'. This distinguishes it from sibling tools like log_cardio and log_meal, and the requirement of an app user context adds further specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'strength or cardio unified' implies this tool is for logging complete workouts that may include both types of sets, which helps differentiate it from log_cardio. It also states a prerequisite (app user context), but does not explicitly mention alternatives or exclusions for cases like updating existing sets or standalone cardio logging.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.