Draft a workout plan. Not medical advice. confirm:true required. Pass health constraints so the draft can honour them.
| Name | Required | Description | Default |
|---|---|---|---|
| goal | No | ||
| health | No | ||
| confirm | Yes | ||
| experience | No | ||
| days_per_week | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate a non-read-only, non-idempotent mutation tool. The description adds that a confirm flag must be true, a critical behavioral prerequisite, and emphasizes that health constraints influence the draft. It stops short of describing side effects or output, but adds meaningful operational context beyond 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?
The description is extremely compact: two sentences front-load the verb and resource. The second sentence packs three critical details (non-medical disclaimer, confirm requirement, health constraints) without wasted words.
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?
The tool has a nested health object, no output schema, and five parameters, yet the description only covers the health and confirm aspects. It does not describe the expected return value, how the other parameters influence the plan, or any side effects beyond drafting. Given the moderate complexity, this is adequate but has clear gaps.
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 0% schema description coverage, the description must compensate. It clarifies that 'confirm' must be true and that the 'health' object should be populated with constraints. However, it does not explain the purpose of 'days_per_week', 'goal', or 'experience', leaving them under-specified.
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 states 'Draft a workout plan' with a specific verb and resource, clearly distinguishing it from siblings like ai_meal_plan or get_workouts. It communicates a generative/creative action rather than logging or retrieving.
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 explicit usage requirements: 'confirm:true required' and 'Pass health constraints so the draft can honour them.' It also adds a medical disclaimer. However, it does not explicitly contrast with alternative tools or state when not to use it.
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.