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scopeguard-mcp

scopeguard-mcp

by Wsh7Ash
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

redact

Strips secrets, tokens, and PII from text before it enters findings, prompts, or reports, returning cleaned text and counts.

Instructions

Strip secrets, tokens and PII from text before it is pasted into a finding, a prompt or a report. Returns the cleaned text and counts.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "text": "<text>"
    },
    "name": "redact"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYesRaw text to clean

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior3/5

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

With no annotations at all, the description carries the full burden. It does disclose the return shape (cleaned text and counts), which is valuable, but it says nothing about irreversibility/destructive nature of redaction, what replacement placeholders look like, possible over-redaction, or whether the original text is preserved anywhere.

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?

Two sentences, zero waste, and the core action is front-loaded ahead of the usage context. Nothing needs trimming.

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

Completeness4/5

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

For a one-parameter utility with no output schema, the description covers what it does, when it applies, and roughly what it returns (text plus counts). It stops short of explaining what the counts mean or what redacted spans are replaced with, which is a minor but real gap.

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?

Only one parameter, and schema coverage is 100% – the schema already defines 'text' as the raw text to clean. The description adds no further syntax, size limits, or format constraints beyond that, so the baseline 3 applies.

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?

States a specific verb (Strip) and specific resources (secrets, tokens, PII) applied to a named input (text). It is unmistakably distinct from the sibling tools (check_target, cvss_base, list_findings, add_finding), which are scanning/rating/finding operations.

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?

Gives a clear trigger context: use it before text is pasted into a finding, a prompt or a report. It does not name explicit alternatives or when-not-to-use conditions, but the usage moment is unambiguous.

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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