securedact-mcp

by GigantesHJI

Overview Schema Source Related Servers Score Discussions Admin

Python

Local

redact_text

Redact sensitive data from text locally using configurable policies. Returns sanitized output for safe downstream use.

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

A4.9/5.0

Behavior5/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It transparently warns that legacy mode 'returns potentially sensitive local-review details' and 'must never be sent to an external service', and specifies the return contents (status, sanitized_text, counts) for normal modes. It also notes local processing, adding essential context about data handling.

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 concisely structured: the first sentence directly states the routing preference, followed by a clear explanation of normal vs. legacy behavior, and finally the return contract. Every sentence earns its place, with no filler or repetition.

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 three parameters and existing output schema, the description covers all necessary context: when to use it, what it returns, the special legacy mode with its sensitive nature, and the difference from its sibling. No critical information is missing for correct invocation.

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%, so the baseline is 3. The description adds meaningful value for response_mode by clarifying that normal modes 'behave like prepare_for_external_ai' and that the legacy value exposes original values, which is not fully captured in the schema. This extra explanation helps an agent avoid misusing the sensitive legacy path.

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 this as a 'Direct/lower-level redaction entry point' and explicitly states it performs 'the same local sanitization as prepare_for_external_ai', distinguishing it from the preferred sibling. It names the action (redact), the resource (text), and explains the optional legacy mode, leaving no ambiguity about what the tool does.

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 gives explicit guidance: 'prefer prepare_for_external_ai for normal outbound workflows' and 'Use redact_text when you specifically need this lower-level compatibility path, or the legacy mode'. It names the alternative tool and the precise conditions for choosing this one, fully satisfying the when/when-not requirement.

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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