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Add meal batch

add_meal_batch
Idempotent

Batch add up to 25 meal entries to your YAZIO diary. In case of partial errors, it reverts successful writes and shows each item's status.

Instructions

Adds up to 25 products sequentially; on partial failure, compensates earlier writes and reports each item result.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsYes
dry_runNo
client_request_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

The description adds valuable non-obvious behavior: sequential processing, partial failure compensation, and per-item result reporting. However, it omits the critical dry_run default behavior (default true in schema), meaning by default no actual writes happen, which is a significant transparency gap not covered by annotations.

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 that packs multiple crucial details (batch limit, sequential execution, compensation, result reporting) without any fluff, and the verb is front-loaded.

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 the batch behavior and failure handling, but misses the dry_run default and idempotency context that are present in annotations and schema. Since an output schema exists, it does not need to explain return values, but the dry_run omission is a significant gap for a mutation tool.

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?

The description provides no information about parameters. With schema coverage at 0%, the agent must rely entirely on the JSON schema, but the schema lacks descriptive text for key fields like 'dry_run' and 'client_request_id', so the description adds no semantic value.

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 action ('Adds'), the resource ('products'), and scope ('up to 25'), distinguishing this batch tool from the single-item sibling 'add_consumed_item'. The behavior is specific and unambiguous.

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 context is clear: it is for batch-adding up to 25 meal items, with sequential execution and result reporting. However, it does not explicitly mention alternatives like 'add_consumed_item' for single items or when not to use this tool, so it stops short of full exclusion guidance.

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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