pii-redact
Server Details
Detects and redacts PII (emails, phones, SSNs, names, addresses) from text. $0.02/call via x402.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
TDQS
Scored across 1 tool
Only one tool exists, so there is no risk of confusion or misselection between tools.
With a single tool named 'redact', the naming is trivially consistent; there are no other tools to conflict.
A single tool covers the core PII redaction functionality, but for a server with this scope, separate detection and redaction tools might be expected, making it slightly thin.
The tool handles detection and redaction in one step, but lacks granular control over which PII types to redact or options for different input formats, so the surface is adequate but not comprehensive.
Available Tools
1 toolredactAInspect
Detect and redact PII in text or JSON. Recognizers are pure pattern/checksum matching (regex + Luhn/mod-97/SSN-area validation) — no LLM calls, no external services. Person names and dates of birth are matched only via nearby context (e.g. 'Mr. Smith', 'DOB: ...'), not general NER, to keep false positives low.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | ||
| options | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It explains recognition method (pattern/checksum, no LLM), context-based matching for names/DOB, and explicitly mentions low false positives. Lacks details on side effects like whether output is a copy or destructively modifies input, but overall good transparency for a PII tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with core purpose, followed by brief technical detail. Every sentence adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given complexity (nested options, 2 params), description covers overall purpose and recognition behavior but misses parameter specifics and output details. With no output schema or siblings, more context would be beneficial for full agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage, and description adds no parameter details. The 'input' parameter is implied by 'text or JSON', but 'options' (mode, locale, entities) are completely absent from description, leaving the agent to infer from enum values alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description explicitly states 'Detect and redact PII in text or JSON', providing a specific verb and resource. It clearly distinguishes the tool's purpose without being generic or tautological.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
While description implies usage for deterministic pattern-based redaction (no LLM/external services), it does not provide explicit when-to-use or when-not-to-use guidance or alternatives, especially given no sibling tools are listed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
- First observed
redact
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Related MCP Servers
- AlicenseNot gradedqualityBmaintenanceDetect PII in text: emails, phones, SSNs, credit cards, IPs, addresses. Pay-per-call via x402 micropayments without API keys or signup.MIT
- FlicenseNot gradedqualityDmaintenanceEnables AI agents to redact PII from text, summarize redacted content, and manage custom redaction patterns across multiple languages.-
- FlicenseAqualityCmaintenanceDetects and redacts sensitive personal information such as names, emails, phone numbers, ID numbers, and locations in text before it is processed by AI agents, using configurable mask, hash, or remove strategies.1-
- FlicenseAqualityBmaintenancePII sanitization layer for autonomous AI agent pipelines. Detects and redacts emails, phone numbers, national IDs, private keys, and financial data before text reaches LLMs. Supports EN, ES (LATAM), PT (BR/PT), DE, JA.12-
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