securedact-mcp

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redact_text

Read-onlyIdempotent

Redact sensitive information from text locally before external AI use. Returns approved sanitized text with status and redaction counts for privacy-safe workflows.

Instructions

Direct/lower-level redaction entry point; prefer prepare_for_external_ai for normal outbound workflows.

In its normal modes this performs the same local sanitization as prepare_for_external_ai and returns the approved result, so most agents should call prepare_for_external_ai instead. Use redact_text when you specifically need this lower-level compatibility path, or the 'legacy' mode for local review of raw redaction internals. The 'legacy' mode returns potentially sensitive local-review details and is never selected by default.

Returns, for normal modes, the same approved result as prepare_for_external_ai (status, sanitized_text, counts). For 'legacy' mode it returns a result with deprecation_code 'legacy_sensitive_response' containing local-review redaction data.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`text`	Yes	Free text to redact locally. Processing is on this machine; nothing is transmitted externally.
`policy`	No	Named redaction policy controlling which entity types are masked or blocked. Defaults to 'default'. Common values include 'default' and 'strict_external_ai'; other policies may be registered. An unknown name returns a policy_not_found error.	default
`response_mode`	No	Normal modes behave like prepare_for_external_ai: 'minimal', 'review', and 'debug' return the approved result with increasing detail. The special value 'legacy' returns raw local-review redaction internals (including a mapping that reveals original values) under deprecation_code 'legacy_sensitive_response'; it must never be sent to an external service. Defaults to 'minimal'.	minimal

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedv0.4.2Aug 25, 2026

addedInput schema / properties / policy / description

Added value: +"Named redaction policy controlling which entity types are masked or blocked. Defaults to 'default'. Common values include 'default' and 'strict_external_ai'; other policies may be registered. An unknown name returns a policy_not_found error."

addedInput schema / properties / response_mode / description

Added value: +"Normal modes behave like prepare_for_external_ai: 'minimal', 'review', and 'debug' return the approved result with increasing detail. The special value 'legacy' returns raw local-review redaction internals (including a mapping that reveals original values) under deprecation_code 'legacy_sensitive_response'; it must never be sent to an external service. Defaults to 'minimal'."

addedInput schema / properties / text / description

Added value: +"Free text to redact locally. Processing is on this machine; nothing is transmitted externally."

Changed1 schema field changedv0.2.0Aug 19, 2026

addedInput schema / properties / response_mode

Added value: +{
+  "default": "minimal",
+  "title": "Response Mode",
+  "type": "string"
+}

First observedv0.1.0Jul 31, 2026

TDQS

A4.7/5.0

Behavior5/5

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

The description adds valuable behavioral context beyond the annotations, noting that normal modes return the same approved result as prepare_for_external_ai and that 'legacy' mode returns sensitive local-review details under a deprecation_code and must never be sent externally. This complements the readOnlyHint and idempotentHint annotations without contradiction.

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 organized and front-loaded with the most critical routing guidance: prefer prepare_for_external_ai. Each paragraph earns its place, covering normal modes, legacy mode caveats, and return behavior without unnecessary filler.

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 that an output schema exists and the annotations cover read-only, idempotent, non-destructive behavior, the description provides sufficient context for selecting and invoking the tool correctly. It explains when to use it, what modes exist, and the sensitive nature of legacy output, leaving no critical gap for an agent.

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 100%, so the input schema already documents all parameters well. The description reinforces the response_mode semantics and legacy sensitivity but does not add substantial new meaning beyond what the schema already provides, so the baseline score 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 identifies redact_text as a lower-level redaction entry point and contrasts it with prepare_for_external_ai, stating that it performs the same local sanitization in normal modes. It also specifies the distinct 'legacy' mode purpose, making its role unambiguous relative to sibling tools.

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?

The description explicitly tells agents to prefer prepare_for_external_ai for normal outbound workflows and to use redact_text only when needing the lower-level compatibility path or 'legacy' mode for local review. This gives clear when-to-use and when-not-to-use guidance, directly addressing the main 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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