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transform_data

Filter, group, aggregate, sort, or select columns from Serbian government datasets. Apply operations like sum, mean, and count to transform raw data.

Instructions

Transform data: filter, group, aggregate, sort, or select columns.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesRow dicts from get_resource_data()
columnNoPrimary column for filter/sort/aggregate
columnsNoColumn list for select
functionNoAggregation function for aggregate (sum, mean, median, min, max, count, std, var)sum
group_byNoColumn(s) for group
ascendingNoSort direction
operationYesOne of: filter, group, aggregate, sort, select
aggregationsNo{column: agg_func} for group

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

With no annotations, the description carries full burden for behavioral disclosure. It only lists operations and does not mention any behavioral traits such as side effects, immutability, or return behavior beyond what the schema provides.

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

Conciseness3/5

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

The description is a single sentence, making it concise, but it lacks crucial details that would help an agent select and invoke the tool correctly. The brevity comes at the expense of informativeness.

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

Completeness2/5

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

Given the tool has 8 parameters, many sibling tools, and no annotations, the description is incomplete. It does not clarify the meta-operation nature, prerequisites, or when to use combined operations.

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

Parameters3/5

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

Schema coverage is 100%, so baseline is 3. The description adds no additional meaning beyond what is in the schema; it does not explain which parameters correspond to which operation or provide usage context.

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

Purpose4/5

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

The description clearly states the tool performs data transformations: filter, group, aggregate, sort, or select columns. It uses a verb and resource, but does not differentiate from sibling tools like filter_data_tool or sort_data_tool, which individually perform each operation.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus the many sibling tools that each handle a single operation. There is no mention of combining operations or when this meta-tool is preferable.

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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