Draft a meal plan. Not medical advice. confirm:true required. Pass health constraints so the draft can honour them.
| Name | Required | Description | Default |
|---|---|---|---|
| days | Yes | ||
| goal | No | ||
| health | No | ||
| confirm | Yes | ||
| diet_type | No | ||
| daily_calories | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnlyHint false, destructiveHint false, openWorldHint true), the description adds 'Not medical advice' and the confirmation requirement, signaling a cautious, user-confirmed generation behavior. It doesn't elaborate on persistence or side effects, but this is adequate given the annotation support.
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 a single, front-loaded sentence that states the purpose first and packs essential cautions and requirements into minimal words. There is no redundancy or fluff.
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 6 parameters, a nested health object, no output schema, and important caveats (confirmation, medical disclaimer), yet the description stays at a high level. It doesn't describe what the meal plan draft includes, optionality of 'health,' or how 'days' and other parameters shape the output. This leaves the agent without enough context to invoke the tool confidently.
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 but only mentions 'health constraints' and 'confirm:true.' It leaves 'days,' 'goal,' 'diet_type,' and 'daily_calories' unexplained, and the schema offers only types, not meanings. This under-explains a 6-parameter tool with a nested object.
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 starts with 'Draft a meal plan,' using a specific verb and resource that clearly distinguishes it from sibling tools like ai_parse_meal or ai_workout_plan. The added caveat 'Not medical advice' clarifies scope without muddling the core purpose.
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 gives explicit operational guidance: 'confirm:true required' and 'Pass health constraints so the draft can honour them.' It does not name alternatives or exclusion criteria, but the tool's name and purpose are self-evident compared to siblings, so the guidance is clear.
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.