Log a meal (calories + macros + optional ingredients) for the current app user.
| Name | Required | Description | Default |
|---|---|---|---|
| date | Yes | ||
| fats | No | ||
| name | Yes | ||
| carbs | No | ||
| protein | No | ||
| calories | Yes | ||
| meal_type | No | ||
| idempotency_key | No |
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | ||
| date | No | ||
| fats | No | ||
| name | No | ||
| carbs | No | ||
| protein | No | ||
| calories | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds minimal behavioral context beyond the annotations. It scopes the operation to the current app user, but does not explain side effects, idempotency behavior, or what happens on duplicate entries. The mention of 'optional ingredients' is not supported by any schema parameter, which could mislead. However, it does not contradict 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?
The description is a single, succinct sentence that front-loads the main action and key details. Every word earns its place, and it is appropriately concise without padding.
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?
Despite having 8 parameters and an output schema, the description is incomplete for a logging tool. It does not explain parameter meanings, required fields, or constraints like date format or meal_type values. The output schema exists, so return values are covered, but the overall context for effective use is severely lacking.
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 carries the parameter-documentation burden, but it only mentions 'calories' and 'macros' (which maps to protein/carbs/fats). It omits required parameters like date and name, and misunderstands the schema by referring to 'optional ingredients' when no ingredients field exists. This provides insufficient parameter clarity.
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 purpose with a specific verb ('Log') and resource ('a meal'), and distinguishes it from sibling tools like update_meal and delete_meal by indicating creation. It also specifies the key content (calories + macros + optional ingredients), making the action unambiguous.
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?
No guidance is provided on when to use this tool versus alternatives such as ai_parse_meal or log_water. The description only states that it logs a meal for the current user, but lacks any when/when-not instructions or alternatives.
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.