get_meals
List the current app user's meals for a date range.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| date_to | No | ||
| date_from | No |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| items | No |
List the current app user's meals for a date range.
| Name | Required | Description | Default |
|---|---|---|---|
| date_to | No | ||
| date_from | No |
| Name | Required | Description | Default |
|---|---|---|---|
| items | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds scope information ('current app user's meals') and date-range filtering, but does not disclose additional behavioral details beyond what annotations (readOnlyHint, idempotentHint) already convey. It does not mention pagination, ordering, or default date behavior, though the annotations cover the safety profile.
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, concise sentence that gets straight to the point with no redundant phrasing. 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 list-retrieval tool with a readOnlyHint and an output schema, the description is adequate and complete enough for an agent to understand the tool's basic function. It could optionally mention that dates are inclusive or describe return structure, but the output schema covers returns, and annotations cover safety.
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 input schema has no descriptions for date_to and date_from, and schema description coverage is 0%. The description only says 'for a date range' without explaining which parameter represents the start or end date, nor the date format expected. This leaves parameter semantics to be inferred from parameter names.
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 function: listing the current app user's meals within a date range. It uses a specific verb ('List') and resource ('meals'), distinguishing it from sibling tools like log_meal and update_meal.
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 provides clear context that this is for retrieving the current user's meals over a date range, implying when to use it. However, it does not explicitly mention alternatives or exclusions, such as when to use search_foods or ai_parse_meal instead.
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.