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PII redaction

parserail_redact

Detect and remove names, emails, phones, SSNs, cards, and PHI from text before storage or logging. Protects sensitive data by redacting personally identifiable information from any input.

Instructions

Detect and strip names, emails, phones, SSNs, cards, and PHI from text before you store or log it. Costs credits from the account wallet.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
typesNoEntity types to redact; omit for all.
placeholderNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.5

TDQS

A4.1/5.0
Behavior4/5

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

The description adds a behavioral detail not present in annotations: 'Costs credits from the account wallet.' It also clarifies the operation as 'detect and strip,' consistent with readOnlyHint=false. No contradiction with annotations exists.

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 a single sentence that front-loads the action and resource, then adds the cost detail. Every clause is informative with no filler or redundancy.

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

Completeness3/5

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

With no output schema, the description should indicate the return value; 'strip... from text' implies a redacted text output, but placeholder behavior and return format are unspecified. The irreversible nature of redaction is not mentioned, which could be a gap for a redaction tool.

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 description coverage is only 33% (only 'types' has a description). The description partially compensates by listing example entity types, but it does not explain 'text' or 'placeholder' semantics beyond their names. Given the low coverage, more parameter guidance would be expected.

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 states a specific verb ('detect and strip') and resource ('text'), listing concrete entity types (names, emails, phones, SSNs, cards, PHI). This clearly differentiates it from sibling tools like parse or extract, making its purpose immediately obvious.

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

Usage Guidelines4/5

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

The phrase 'before you store or log it' provides a clear usage context, implying when redaction should be applied. However, it does not explicitly mention alternatives or exclusions, though the specific use case helps distinguish it from other text-processing tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.