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suggest_cleaning

Analyze a dataset profile and return recommended cleaning operations like handling nulls, duplicates, and whitespace, with rationale and priority.

Instructions

Profile a dataset and return specific recommended cleaning operations.

Analyses the profile and suggests operations with rationale, e.g.:
  - "col 'age' has 12% nulls → consider fill_na or drop_na_rows"
  - "7 duplicate rows detected → apply drop_duplicates"
  - "col 'name ' has leading/trailing whitespace → apply strip_whitespace"

Args:
    path: Absolute local path to the dataset file.

Returns JSON with profile summary and a list of suggested operations,
each with: operation, column (if applicable), rationale, priority (high/medium/low).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Without annotations, the description explains the return structure (JSON with suggestions) and the process (analyzing profile). It does not mention side effects, but the tool appears read-only. No contradictions.

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 well-structured with clear sections, examples, and a concise return description. Every sentence adds value, and no information is repeated or wasted.

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?

Given the simple input (single path) and output schema (profile + suggestions with rationale/priority), the description adequately covers functionality. Minor missing details on error handling or supported formats, but generally complete.

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

Parameters5/5

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

The schema only has a title for 'path', but the description adds an explicit description: 'Absolute local path to the dataset file.' This compensates for the 0% schema coverage and fully clarifies the parameter.

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 clearly states it profiles a dataset and returns specific recommended cleaning operations, distinguishing it from siblings like 'profile_dataset' (just profile) and 'clean_dataset' (applies cleaning). Examples illustrate the output.

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

Usage Guidelines3/5

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

Usage is implied but not explicitly compared to alternatives. No when-not-to-use or prerequisites are mentioned. The examples hint at the scope but lack explicit guidance.

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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