transform_data

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, but it only names transformation categories. It does not state whether the input table is modified in place, whether output_table is required to avoid side effects, or what happens if an operation is invalid or unsupported. The operation list provides minimal behavioral context but leaves major effects undisclosed.

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 text is extremely short, which is concise, but it is a fragment rather than a well-structured definition. It omits the necessary usage and behavior content, so the brevity reflects under-specification rather than effective density.

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?

For a tool with three parameters, no annotations, and many siblings, the description is far from complete. It does not explain how to construct the operations string, the role of optional output_table, or the effect on the source table. The presence of an output schema reduces the need to describe return values, but the core invocation contract is still missing.

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 must compensate. It partly does by enumerating candidate values for operations (normalize, fillna, extract dates, onehot, bin, derive), but it does not explain the expected string format, separators, per-operation arguments, or the meaning of table_name and output_table.

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 tool name and the Russian fragment indicate that it applies transformations to data, and the list names specific operations (normalize, fillna, extract dates, onehot, bin, derive), so it is not a pure tautology. However, it lacks a full sentence stating what the tool does, what input it acts on, and how it differs from sibling data-preparation tools like segment_data or merge_tables.

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?

There is no guidance on when to use this tool versus alternatives such as sql_query, profile_data, or other siblings. No contexts, exclusions, or prerequisites are mentioned; the agent must infer applicability solely from the list of transformation names.

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