log_water
Log water intake (ml) for the current app user.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| date | Yes | ||
| amount_ml | Yes | ||
| idempotency_key | No |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | ||
| ml | No | ||
| date | No |
anatome
by dev.anatome
TDQS
A3.9/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=false (write operation) and idempotentHint=false. The description adds that it logs for the current app user but does not disclose behavior like appending records or the role of idempotency_key. With annotations covering the safety profile, a mid-range score is appropriate.
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 concise sentence that is front-loaded with the verb and resource. Every word earns its place with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a simple logging tool with an output schema and annotations providing safety hints. The description states the essential action and scope. It is complete enough for expected usage, though it could benefit from a note about idempotency or typical call patterns.
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?
Schema description coverage is 0%, and the description provides minimal parameter meaning beyond the unit 'ml'. It does not explain the date format, the expected range or type of amount_ml, or the purpose of idempotency_key, leaving the agent to rely on param names only.
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 uses a specific verb (Log), names the resource (water intake), specifies the unit (ml), and identifies the scope (current app user). It clearly distinguishes from sibling tools like get_water (read) and other log_* tools.
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 provides clear context by indicating the action and scope, making it obvious when to use this tool. However, it does not explicitly mention alternatives or when not to use it, though this is less critical for a simple logging tool.
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.