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auto_clean

Inspect data for quality issues and duplicates, then receive a proposed cleaning plan with actions like drop columns, replace sentinels, and dedupe. Plan-only, no data modification.

Instructions

Inspect quality + duplicate findings and PROPOSE a cleaning plan.

    **Plan-only — does NOT modify data.** Returns Markdown plan + structured
    ops list. Caller reviews and (optionally edits then) passes ops into
    `clean_pipeline` to actually execute.

    Auto-maps: constant cols → drop, all-null cols → drop, sentinel values
    → replace with NULL, type-drift → TRY_CAST, exact duplicates → dedupe.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
source_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations, the description carries the full burden and excels. It explicitly states 'Plan-only — does NOT modify data,' and details the return format (Markdown plan + structured ops list) and the auto-mapping logic, making behavioral expectations clear.

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 and front-loaded with the core purpose. It uses concise bullet-style lines for the auto-mapping rules, avoiding unnecessary fluff while delivering key information efficiently.

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

Completeness5/5

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

Given the tool has only one parameter and an output schema exists (which covers return structure), the description covers all essential aspects: purpose, side effects, output format, and follow-up actions. It is complete and self-contained for the tool's complexity.

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

Parameters2/5

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

Schema coverage is 0% and the description does not mention the 'source_id' parameter at all. The name is self-explanatory to some degree, but the description provides no additional context or constraints, leaving the agent to guess what a valid source_id looks like.

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 opens with 'Inspect quality + duplicate findings and PROPOSE a cleaning plan,' which is a specific verb+resource combination. It clearly distinguishes itself from execution tools like clean_pipeline by emphasizing 'Plan-only — does NOT modify data.' The auto-mapping rules further clarify its scope.

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

Usage Guidelines5/5

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

Explicitly states when to use this tool: for proposing a cleaning plan, and directs the caller to pass the ops to 'clean_pipeline' for execution. It also clearly indicates what it does NOT do (does NOT modify data), providing a clear when-not scenario and an alternative.

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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