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load_parquet

Load a Parquet file into memory for analysis. Uses columnar format, faster than CSV for large datasets.

Instructions

Load a Parquet file into memory. Parquet is columnar and much faster than CSV for large files. The name defaults to the filename. Example: load_parquet(file_path="data.parquet")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
file_pathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It discloses that the file is loaded into memory and that the name parameter defaults to the filename, adding useful behavioral context. However, it does not discuss memory implications, error behavior, or whether the operation is read-only beyond the implied 'load' action.

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 three sentences, each earning its place: the first states the purpose, the second provides rationale for preferring Parquet, and the third gives a default behavior and example. It is appropriately sized and front-loaded, with zero 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?

For a simple load tool, the description covers all essential aspects: what it does, when to use it, an example, and a default behavior. An output schema exists, so return values need no explanation. It lacks caveats about file size or format specifics, but this is adequate for the tool's simplicity.

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?

Schema description coverage is 0%, so the description must explain the parameters. It does so by providing an example using file_path and explicitly stating that name defaults to the filename. This gives meaning to both parameters, though it does not fully elaborate on the purpose of the name parameter beyond its default.

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 starts with 'Load a Parquet file into memory,' a specific verb+resource statement that clearly defines the tool's function. It also distinguishes itself from sibling load tools by explicitly naming the Parquet format, setting it apart from load_csv and load_excel.

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 usage context by stating that Parquet is columnar and much faster than CSV for large files, implying when this tool should be preferred over load_csv. However, it does not explicitly name alternative tools or mention when not to use it, so it lacks 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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