List the current app user's body metrics / measurements (weight, height, bicep, body fat, etc.), most recent first. Filter by metric_type (e.g. 'weight', 'bicep') and/or a date range. Requires an app user context.
| Name | Required | Description | Default |
|---|---|---|---|
| to | No | ||
| from | No | ||
| limit | No | ||
| metric_type | No |
| Name | Required | Description | Default |
|---|---|---|---|
| items | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint=false, and idempotentHint, so the safety profile is covered. The description adds valuable behavioral details: ordering (most recent first), filtering options, and the requirement for an app user context. This goes beyond annotations, though it doesn't mention pagination or edge cases like empty results.
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 sentences, front-loaded with the action and resource. Every phrase adds value: examples, ordering, and filter capabilities. No redundant or filler content.
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 4 optional parameters, presence of an output schema, and strong annotations, the description covers the core functionality, filter options, and context requirement. It does not need to explain return values due to output schema, and the missing details (e.g., limit behavior) are inferable from defaults. Slightly more edge-case info (e.g., pagination) would push it to 5.
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 must compensate. It explains metric_type with examples and implies the meaning of from/to via 'date range', but does not explicitly describe the 'limit' parameter or date formats. This is helpful but incomplete for all parameters, leaving some interpretation to the agent.
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 it lists body metrics/measurements for the current user, with specific examples (weight, height, bicep, body fat). It distinguishes itself from sibling tools like log_body_metric (write operation) and get_progress (different resource). The verb 'List' plus resource 'body metrics' makes the purpose unambiguous.
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?
It provides clear context on when to use: retrieving the current app user's body metrics, with optional filtering by metric_type and date range. It also states a prerequisite: requires an app user context. However, it does not explicitly name alternatives or exclusion criteria, so it lacks explicit when-not-to-use guidance, but the context is strong.
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.