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export_raw_csv

Export raw CSV data from Cronometer for nutrition tracking, including servings, daily summaries, exercises, biometrics, or notes. Specify date range and data type to download unprocessed records.

Instructions

Export raw CSV data from Cronometer for any data type.

Useful when you need the full unprocessed export.

Args: export_type: One of 'servings', 'daily_summary', 'exercises', 'biometrics', 'notes'. start_date: Start date as YYYY-MM-DD (defaults to today). end_date: End date as YYYY-MM-DD (defaults to today).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
export_typeYes
start_dateNo
end_dateNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions the tool exports data but doesn't disclose behavioral traits like authentication requirements, rate limits, file size considerations, or what 'raw CSV' specifically entails (e.g., format details, headers). The description is minimal beyond the basic operation.

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 appropriately sized and front-loaded: the first sentence states the core purpose, the second provides usage context, and the Args section efficiently documents parameters. Every sentence earns its place with no wasted words.

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?

Given the tool's moderate complexity (data export with date ranges), no annotations, but an output schema exists, the description is reasonably complete. It covers purpose, usage context, and parameters well, though lacks behavioral details like auth or limits. The output schema likely handles return values, reducing need for description.

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?

With 0% schema description coverage, the description fully compensates by explaining all three parameters: export_type with specific enum values, start_date with format and default, and end_date with format and default. This adds essential meaning beyond the bare schema.

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's purpose with a specific verb ('Export') and resource ('raw CSV data from Cronometer'), and distinguishes it from siblings by emphasizing 'full unprocessed export' which differentiates it from processed data tools like get_daily_nutrition or get_food_log.

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 for when to use this tool ('when you need the full unprocessed export'), but doesn't explicitly state when not to use it or name specific alternatives among the sibling tools, though the 'unprocessed' distinction implies alternatives exist.

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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