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ThinkNEO MCP SMB

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guardrails_scan_pii

Read-onlyIdempotent

Scan text for personally identifiable information (PII) including emails, phone numbers, tax IDs, SSNs, and credit cards. Use it to audit content before logging or sharing to comply with GDPR and LGPD.

Instructions

Scan text for personally identifiable information (PII) across US, international, and Brazilian formats.

Detects 7 PII types: email addresses, US phone numbers, international
phone numbers (E.164-style with country code), Brazilian CPF and CNPJ
tax IDs, US Social Security Numbers, and 16-digit credit card numbers.
Use this to audit content before logging, storing, exporting, or
sharing it — e.g. as a GDPR/LGPD pre-storage check. Deterministic
regex engine; read-only and safe to retry.

Returns a JSON object:
  - pii_detected (bool): true if any PII was found.
  - findings (list): one object per PII type found, with
    "pii_type" (str: "email" | "phone" | "phone_intl" | "cpf" |
    "cnpj" | "ssn" | "credit_card"), "count" (int: occurrences), and
    "redacted_samples" (list of str: first 3 matches, truncated to 3
    leading characters + "***" so no raw PII is echoed back).

Example: guardrails_scan_pii(text="Contact joe@acme.com or
555-123-4567") returns pii_detected true with findings for "email"
(count 1, sample "joe***") and "phone" (count 1, sample "555***").

Billing note: on the hosted ThinkNEO endpoint this call costs 1 TNC;
this open-source build runs free and offline.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe text to scan for personally identifiable information: user input, chat messages, documents, log lines, or any string that might contain emails, phone numbers, government IDs, or card numbers. Up to 50,000 characters.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations already indicate readOnlyHint=true and idempotentHint=true. Description adds 'deterministic regex engine', 'read-only and safe to retry', and billing info (free offline, costs 1 TNC on hosted). No contradictions.

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?

Well-structured: summary sentence, then detailed lists, return format, example, and billing note. No wasted sentences; front-loaded with key info.

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 simple input (1 required param) and presence of output schema, description covers input semantics, return structure, example, and usage context completely.

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% with description for 'text' parameter. Description adds context like 'user input, chat messages, documents, log lines' and character limit (50,000), enhancing schema meaning.

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?

Description clearly states 'Scan text for personally identifiable information (PII)' and lists 7 specific PII types. Distinguishes from sibling tools (check, injection, secrets) by focusing exclusively on PII.

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?

Provides explicit use case: 'audit content before logging, storing, exporting, or sharing' as a GDPR/LGPD check. Mentions read-only and safe to retry. Does not explicitly exclude scenarios or name alternatives, but context is clear.

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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