Browse the curated workout routine library (PPL, upper/lower, full-body, 5x5, etc.). Filter by difficulty, goal, split_type, days_per_week. Returns minimal list.
| Name | Required | Description | Default |
|---|---|---|---|
| goal | No | ||
| limit | No | ||
| difficulty | No | ||
| split_type | No | ||
| days_per_week | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the agent knows it is a safe read operation. The description adds the facts that the library is 'curated' and that the response is a 'minimal list', which provides useful behavioral context beyond the annotations. However, it does not elaborate on output structure or pagination, so the added transparency is moderate.
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 totaling about 25 words. It opens with the verb 'Browse', immediately states the resource and key filters, then closes with the return characteristic. Every sentence earns its place with 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?
For a simple read-only list operation with no output schema, the description covers the core aspects: purpose, filter choices, and return minimality. It does not mention the 'limit' parameter or its default of 20, but this information is available in the input schema. Given the good annotations and relative simplicity of the tool, the description is sufficiently complete, though it could briefly note the limit behavior.
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 schema description coverage at 0%, the description carries the burden of parameter clarification. It names four of five filter parameters (goal, difficulty, split_type, days_per_week) and indicates they serve as filters, which adds context beyond the bare schema. However, it omits the 'limit' parameter and does not explain filter combinations or value meanings, relying on self-explanatory enums.
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 the specific verb 'Browse' to indicate the action of viewing the workout routine library, lists concrete filter dimensions (difficulty, goal, split_type, days_per_week), and notes the return type ('minimal list'). This clearly distinguishes it from siblings like get_routine or ai_workout_plan, which focus on a single routine or AI generation, respectively.
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 usage for browsing/filtering the curated library, but it does not explicitly state when to use this over alternatives such as get_routine for specific routines or ai_workout_plan for AI-generated plans. There is no mention of exclusions or when not to use, so the guidance remains implied rather than explicit.
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.