Delete a logged workout and its sets by id (soft delete, 30-day recovery window).
| Name | Required | Description | Default |
|---|---|---|---|
| workout_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| deleted | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the destructiveHint annotation, the description adds crucial behavioral context: it is a soft delete with a 30-day recovery window and it cascades to sets. This provides the agent with important expectations about the operation's effects and reversibility, fully complementing the 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?
A single, front-loaded sentence that immediately communicates the primary action and key details (sets, soft delete, recovery window). Every word earns its place; no filler or repetition.
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 one-parameter destructive tool with an output schema and annotations indicating non-read-only and destructive behavior, the description provides the essential additional context (cascade to sets, soft-delete recovery window). It is complete and appropriately scoped.
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 schema has one parameter (workout_id) with no description, and the description covers 0% of it. The phrase 'by id' adds minimal meaning since the parameter name already implies the workout identifier. No additional format, semantics, or edge-case handling is disclosed, so the description fails to compensate for the low schema coverage.
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 action ('Delete a logged workout and its sets') and the resource ('workout') by id. It distinguishes itself from sibling tools like delete_meal by specifying the scope (workout and its sets) and the soft-delete behavior.
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 the use case: when you need to delete a logged workout with its sets. However, it does not explicitly state when not to use it (e.g., for deleting a single set via update_sets) or mention alternatives. Guidance is implied but not fully developed.
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.