Log a body measurement. metric_type: weight (kg), height (cm), body_fat (%), muscle_mass (kg), waist/chest/bicep/forearm/hip/thigh/calf/neck/shoulder (cm), bmi, resting_hr (bpm). Logging weight enables bodyweight-inclusive volume analytics.
| Name | Required | Description | Default |
|---|---|---|---|
| date | Yes | ||
| unit | Yes | ||
| notes | No | ||
| value | Yes | ||
| metric_type | Yes | ||
| idempotency_key | No |
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | ||
| date | No | ||
| value | No | ||
| metric_type | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate a non-read-only write operation. The description adds the consequence that logging weight enables bodyweight-inclusive volume analytics, but it does not disclose other behavioral details such as idempotency behavior, despite the presence of an idempotency_key parameter. No contradiction with annotations.
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 concise and front-loaded with the action ('Log a body measurement'). The list of metric types is efficiently packed into a single sentence, and the note about weight analytics is a valuable addition without unnecessary fluff.
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?
Given the output schema and annotations, the description covers the most critical parameter semantics (metric_type and unit) and provides a use-case for weight logging. Minor gaps remain for date format, value constraints, and idempotency handling, but these are partly addressed by the schema's basic type information.
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?
With zero schema description coverage, the description compensates by enumerating all metric_type options with their corresponding units, which is essential for correct invocation. However, parameters like date, value, notes, and idempotency_key are not elaborated beyond their schema types, leaving some gaps.
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 'Log a body measurement' and enumerates the supported metric types with their units, making it distinct from sibling tools like log_meal and log_workout. It leaves no doubt about the resource and action.
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 description implies use for recording body metrics and provides a specific benefit for logging weight, but it does not explicitly state when to use this tool over alternatives or mention any exclusions. The domain is clear, but there is no direct guidance.
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.