ai_parse_meal
Parse free-text meal description ('2 eggs and toast with butter') into structured items with grams + macros.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| locale | No |
anatome
by dev.anatome
TDQS
A3.8/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds output context ('grams + macros') beyond the annotations, which is helpful. But it does not address the readOnlyHint=false flag or clarify whether the AI call has side effects. The openWorldHint is already in annotations, so the description provides only marginal additional behavioral transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, front-loaded with the verb, and clearly states input and output. Every word adds value with no filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is no output schema, so the description should explain the return structure more fully. It mentions 'structured items with grams + macros' but does not provide an example output or clarify the role of locale, leaving some ambiguity for a tool with only two parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description needs to compensate. It implicitly describes the 'text' parameter with an example ('2 eggs and toast with butter') but completely omits the optional 'locale' parameter, leaving half the parameters undocumented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool parses free-text meal descriptions into structured items with grams and macros. The specific verb 'Parse' and resource definition distinguish it from siblings like ai_photo_macros (photo input) and ai_meal_plan (meal planning).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implicitly communicates when to use the tool: when you have free-text meal description to convert. However, it does not explicitly name alternatives or exclusion scenarios, such as not for photo-based parsing or structured logging.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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TDQS
A3.7/5.0
Disambiguation4/5
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.
Naming Consistency4/5
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.
Tool Count3/5
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.
Completeness4/5
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.