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clidey

whodb-cli

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whodb_audit

Read-onlyIdempotent

Run data-quality checks to detect null-rate spikes, missing primary keys, low-cardinality issues, duplicate rows, and orphaned foreign keys in a schema or table.

Instructions

Run data-quality checks on one schema or table.

Best for: Finding null-rate spikes, missing primary keys, low-cardinality issues, duplicate rows, and orphaned foreign keys. Not recommended for: Replacing a full observability or data-governance system. Common mistakes: Forgetting to scope the audit to one table when you only need one table.

Usage Example:

{
  "name": "whodb_audit",
  "arguments": {
    "connection": "mydb",
    "schema": "public",
    "table": "orders",
    "null_warning": 15,
    "null_error": 60
  }
}

Returns: Audit results per table, including issue summaries and the underlying table/column findings.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
connectionYesConnection name (optional if only one exists)
schemaNoSchema or database name override
tableNoOptional table name
null_warningNoWarning threshold for null percentage
null_errorNoError threshold for null percentage

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
summaryYes
resultsYes
errorNo
request_idNo
Behavior4/5

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

Annotations already indicate readOnlyHint=true and idempotentHint=true. The description adds behavioral context by listing the types of issues detected and mentioning the return format, but does not contradict annotations.

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

Conciseness4/5

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

Well-structured with clear sections (Best for, Not recommended, Common mistakes, Usage example). Concise but includes valuable example; could be slightly more compact.

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's complexity (5 parameters, no nested objects, output schema exists), the description is thorough: covers purpose, usage guidelines, common mistakes, and an example. No gaps.

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% (all parameters described). The description adds a usage example that illustrates parameter usage, but the schema itself already provides adequate meaning. Baseline of 3 is appropriate.

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 runs data-quality checks on one schema or table and lists specific issues (null-rate spikes, missing primary keys, etc.). This distinguishes it from sibling tools like whodb_query or whodb_suggestions.

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?

Provides explicit 'Best for' and 'Not recommended for' sections, common mistakes, and a usage example. This gives clear guidance on when and how to use the tool.

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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