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sensitive_data_scan

Identify columns containing passwords, credit cards, SSN, and emails across all database schemas to aid GDPR compliance and security reviews. Optionally scan a single schema.

Instructions

Find PII/PHI columns - passwords, credit cards, SSN, emails.

LEVEL: Database (scans all schemas by default) or Schema (if specified)

USE FOR: finding sensitive data, GDPR compliance, security review. DO NOT USE FOR: permissions (security_audit), data quality (data_quality_report).

Examples: sensitive_data_scan() - Scan all schemas sensitive_data_scan(schema='public') - Public schema only

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
schemaNoSchema name to scan. Omit or pass null to scan ALL schemas.
formatNoOutput formatjson
urlNoDatabase URL

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations provided, but description implies read-only by 'Find' and scope (database or schema). Examples clarify behavior. Lacks explicit mention of read-only or auth needs, but sufficient for a scan tool.

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?

Description is concise with clear sections: purpose, level, usage guidelines, examples. No unnecessary words.

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?

All parameters documented, output schema exists (so return values not needed), usage and scope fully explained. Complete for a scanning tool.

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

Parameters4/5

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

Schema coverage is 100% and descriptions are good. Description adds value by clarifying default behavior for schema parameter and providing examples, though baseline is 3 due to high coverage.

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 the tool finds PII/PHI columns (passwords, credit cards, SSN, emails), specifying verb and resource. It also distinguishes from sibling tools like security_audit and data_quality_report by listing what not to use.

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 'USE FOR' and 'DO NOT USE FOR' sections with alternative tools (security_audit, data_quality_report), and examples for common use cases.

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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