get_profile
Get the current app user's fitness profile (height, weight, macro + water goals, diet, active sports). Requires an app user context.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Get the current app user's fitness profile (height, weight, macro + water goals, diet, active sports). Requires an app user context.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint and idempotentHint. The description adds valuable context beyond that: the requirement for an app user context and the specific fields included. This gives the agent a good sense of what the operation entails, though it doesn't discuss error cases or response format, which is acceptable given the simplicity.
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 core purpose, and every word earns its place. The parenthetical field list is a compact, useful addition.
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?
Despite no output schema, the description lists the likely return fields (height, weight, goals, diet, sports) and the required context. This gives a complete picture for a zero-parameter read-only tool. No mention of pagination or errors is necessary here.
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?
The tool has zero parameters, and the schema already reflects this (100% coverage). Per guidelines, the baseline for no parameters is 4. The description adds the relevant context about what the profile contains, which is sufficient.
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 uses a specific verb ('Get') and resource ('current app user's fitness profile') and enumerates the exact contents (height, weight, macro + water goals, diet, active sports). This clearly distinguishes it from sibling get_* tools like get_body_metrics or get_progress.
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 provides clear context by specifying this is for the current app user's profile and states a clear prerequisite: 'Requires an app user context.' It does not explicitly name alternatives or when-not-to-use scenarios, but the scope is clear enough for a simple getter.
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.