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by com.allthepossibles
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Personal data scan

scan_pii
Read-only

Scan text for real personal and secret data that should not appear in design files, and return a masked report plus a redacted copy. Pass text (for example every text layer from a Figma frame, a spec, mockup sample data or OCR text from a screenshot) and optionally mode: design (default) or strict, which also flags capitalised full names next to labels like Customer. Detects emails (example.com/org/net and test/invalid TLDs are treated as safe), phone numbers (fictional 555-01xx and Ofcom drama ranges are safe), payment cards checked with Luhn (public test cards such as 4242 4242 4242 4242 are safe), IBANs checked with mod-97, US SSNs (invalid ranges ignored), UK National Insurance numbers, IPv4 and IPv6 addresses outside documentation ranges, AWS, Stripe, GitHub, Slack, Google, Anthropic and OpenAI keys, JWTs and private key blocks, and lower-confidence street addresses and labeled dates of birth. Each finding has type, masked preview (never the full secret), line, confidence and a safe placeholder; redacted is the input with every finding replaced. The text is processed in memory, never stored or logged. Use it as the last step before sharing a file.

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": "scan_pii"
  }
}

Input Schema

NameRequiredDescriptionDefault
modeNodesign (default) skips name detection and bare 7-digit phone numbers; strict adds them and accepts more false positives.
textYesThe text to scan. Up to 100,000 characters on the free tier.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already cover read-only and non-destructive, yet the description adds a lot: processing is in-memory, never stored or logged; findings include masked previews never full secrets; safe placeholder per finding. Detection rules (Luhn, mod-97, safe ranges) add real behavioral depth beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with purpose and inputs before the long detection catalogue, which is dense but informative. The detection list is long; a slightly shorter summary would preserve clarity, but every sentence carries signal.

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?

No output schema, so the description must describe returns — and it does: finding fields (type, masked preview, line, confidence, placeholder) and the redacted copy. Processing and privacy behavior are covered, leaving nothing an agent needs to call it correctly.

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, but the description genuinely extends the mode enum by explaining what strict adds (capitalised full names next to labels like Customer) and supplies usage examples for text. Adds value beyond the schema.

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 (scan), resource (text for personal/secret data), and two concrete outputs (masked report plus redacted copy). No sibling tool does PII detection, and the design-file framing makes its niche unambiguous.

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?

Explicitly says to use it as the last step before sharing a file, and gives input examples (Figma text layers, spec, mockup sample data, OCR text). It doesn't list when-not-to-use or alternatives, but no sibling overlaps, so context is largely complete.

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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