transform_data

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

With no annotations, the description carries the full burden. It explains the two operational modes and specific parameter behavior (omit field for flat arrays). However, it lacks disclosure of side effects, error handling, performance implications, or output format expectations.

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

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

The description is exceptionally concise—two sentences that front-load the general purpose and then dive into specifics. Every sentence adds value without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 5 parameters and no annotations, the description adequately covers two major use cases but lacks details on edge cases, return structure (though an output schema exists), and behavior for all combinations of parameters.

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

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

Schema description coverage is 0%, so the description adds critical meaning: it explains operation values, field omission rule, and output_format necessity. Yet input_format and data format expectations remain unspecified, leaving some parameters underdocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states that the tool transforms or aggregates tabular/record data, and specifies two modes: conversion and aggregation. It provides operation names and distinguishes between them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit guidance on when to use conversion (operation='convert') versus aggregation operations, and includes a special case for flat arrays. Although no comparisons to sibling tools are needed, it provides clear operation-specific context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.