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log_meal

Log what the user ate to their food diary. Parse the user's free text into items and, when you can, include estimated macros per item for accuracy. SAFETY: all calorie and macro values here — including carbohydrates — are ESTIMATES (from USDA / Open Food Facts or the user's own entry), approximate and not lab-measured or per-batch. They are for general nutrition tracking ONLY. Do NOT use them for insulin dosing, carb-counting for a bolus, blood-glucose prediction, or any other medical decision, and do NOT compute a dose or glucose estimate from them. For anything medical, direct the user to the product's own label and their care team.

Input Schema

TableJSON Schema

Name	Required	Description
`at`	No	ISO-8601 instant the meal was eaten; defaults to now.
`meal`	No
`note`	No
`items`	Yes
`source`	No	Optional provenance for these items. After search_foods/lookup_barcode, pass the candidate's source class (e.g. 'usda' or 'off') so the diary shows it's grounded. Defaults to 'client' (your own estimate). Unrecognized values are recorded as 'client'.
`local_date`	No	YYYY-MM-DD diary date; defaults to the user's local date (from their timezone). Pass this to log a meal on a different day.

TDQS

A4.2/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses a crucial behavioral trait: all macro values are estimates and must not be used for medical decisions. This goes beyond the annotations (which only show it is non-read-only) and adds substantial context about safety and data reliability.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The main action is front-loaded in a clear, single sentence, and the parsing guidance is concise. The safety disclaimer is lengthy but necessary for a nutrition tool, and its structured warning adds value without being overly repetitive.

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?

The description covers input behavior and safety well, but since there is no output schema, it omits what the tool returns (e.g., the logged meal's ID). This could hinder an agent that needs to reference the created meal with sibling tools. It is otherwise adequate for a write operation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description explains that the tool parses free text into items and should include estimated macros, giving practical meaning to these key parameters. With schema coverage at only 50%, this helps compensate for the missing parameter-level descriptions, though it doesn't cover every parameter.

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 immediately states the action: 'Log what the user ate to their food diary,' with a specific verb and resource. It also mentions parsing free text into items, distinguishing it from siblings like update_meal and delete_meal. This leaves no ambiguity about the tool's function.

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?

It clearly implies use for logging food intake and provides instructions on how to parse text and include macros. However, it does not explicitly state when not to use it or mention alternatives like update_meal, so it lacks explicit exclusions.

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

A4.5/5.0

Disambiguation5/5

Each tool targets a distinct resource and action: pantry vs diary vs food search vs preferences vs diagnostics. Even similar tools like get_day/get_range are clearly differentiated by scope, and search_foods/lookup_barcode are distinguished by input type (text vs barcode).

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with lowercase snake_case (add_pantry_item, get_day, log_meal, search_foods). The only exception is whoami, which is a standard diagnostic convention and does not disrupt the overall consistency.

Tool Count5/5

With 12 tools, the server is well-scoped for its food-tracking domain. Each tool covers a necessary function (pantry CRUD, diary CRUD, food search, preferences, diagnostics) without redundancy or bloat.

Completeness5/5

The tool set provides full lifecycle coverage for the core domain: pantry items can be added, read, and removed (upsert covers update); diary entries can be created, read (single/day/range), updated, and deleted; food lookup includes text search and barcode; and preferences are accessible. No obvious gaps hinder agent workflows.

Resources

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