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pbi_detect_dirty_dates

Detects text columns containing malformed date values to ensure data consistency and enable reliable date-based analysis.

Instructions

Detect text columns that look like dirty dates.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tableNo
max_samplesNo
scan_all_text_columnsNo
min_parse_success_rateNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations, the description must disclose behavioral traits. It does not mention whether the tool modifies data, returns results, or requires specific permissions. The term 'detect' implies read-only, but this is not explicitly stated.

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

Conciseness2/5

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

The description is short (one sentence), but it is under-specified. Conciseness is achieved at the expense of completeness, making it less helpful.

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

Completeness1/5

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

Given the tool has four parameters and no annotations or output schema visible in the input, the description is severely incomplete. It lacks essential details about parameters, return values, and behavior.

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

Parameters1/5

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

The description does not explain any of the four input parameters. Schema coverage is 0%, so the description adds no value beyond the parameter names, leaving the agent to guess their meaning (e.g., 'max_samples', 'min_parse_success_rate').

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 action ('detect') and the target ('text columns that look like dirty dates'). It is specific and unambiguous, but does not differentiate from other detection tools in the sibling list, such as pbi_detect_circular_dependencies.

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 alternatives or what prerequisites are needed. The description only states what it does, leaving the agent without context for appropriate invocation.

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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