resolve_exercise
Resolve an exercise name into primary/secondary muscle layers for rendering.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| exercise | Yes |
Resolve an exercise name into primary/secondary muscle layers for rendering.
| Name | Required | Description | Default |
|---|---|---|---|
| exercise | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, covering the safety profile. The description adds useful context about the output structure ('primary/secondary muscle layers') and the rendering purpose, but it does not disclose behavior for unknown exercise names or edge cases. It provides moderate additional context without contradicting 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 efficiently conveys the operation, input, and output. There is no redundancy or unnecessary detail; every word contributes to understanding the tool's purpose.
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 tool with one parameter and no output schema, the description provides enough to grasp the core purpose and expected output type. It leaves open questions about return structure and handling of unknown names, but given the openWorldHint and simple nature, it is largely complete.
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. The phrase 'exercise name' clarifies that the parameter is a name, not an ID, but no examples, format rules, or constraints are given. This is minimal added value beyond the schema.
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 tool's action ('resolve'), the resource (exercise name), and the output (primary/secondary muscle layers for rendering). This specificity distinguishes it from siblings like get_exercise, which retrieves full exercise data, and search_exercises, which finds exercises by criteria.
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 phrase 'for rendering' implies when to use it, but there is no explicit comparison to alternatives or exclusions. It doesn't say 'use this instead of get_exercise' or clarify when not to use it, leaving the usage context somewhat implied rather than stated.
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.