MCP PII Tools
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- FlicenseNot gradedqualityBmaintenanceEnables reversible anonymization of sensitive PII such as SSNs, credit cards, passport numbers, emails, and phone numbers before sending prompts to external LLMs, then restores original values locally using HMAC isolation.7-
- AlicenseNot gradedqualityAmaintenanceEnables local, in-memory sanitization of sensitive text before LLM ingestion by replacing PII, PHI, and credentials with deterministic reversible tokens, then restoring the originals in responses. It also audits content for exposed secrets and PII and provides compliance receipts without external network egress or disk writes.35 npm1Apache 2.0
- FlicenseNot gradedqualityBmaintenanceEnables reversible anonymization of SSNs, credit cards, passport numbers, emails, and phone numbers in prompts before sending them to external LLMs, and restores original tokens from completions using local HMAC isolation.7-
- FlicenseNot gradedqualityBmaintenanceEnables local zero-knowledge encryption of a personal vault and sanitization of PII such as credentials, credit cards, and addresses before dispatch to cloud LLMs, with client-side rehydration of masked data.7-
- AlicenseBqualityAmaintenanceEnables local, in-memory de-identification of PII, PHI, and credentials with deterministic reversible surrogate tokens before LLM transmission, then restores originals from volatile session mappings and audits for exposures without network egress or disk writes.5261 npmApache 2.0
- AlicenseNot gradedqualityCmaintenanceStrips personal data and credentials from text before it reaches a model, log, or ticket, using checksum-verified detection for cards, IBANs, routing numbers, SSNs, contact details, and API keys. Placeholders such as <EMAIL_1> can later be swapped back in so the model's response is restored with the original values.4 npmMIT
TDQS
Scored across 8 tools
There is significant functional overlap between tools, particularly mcp_encrypt_text_pii and mcp_process_text which both combine detection with another operation, and mcp_decrypt_pii_item versus mcp_decrypt_text_pii which differ in scope but share core purpose. However, the descriptions help clarify the distinctions, preventing complete confusion.
All tools follow a consistent mcp_verb_noun naming pattern with snake_case throughout. The verbs (anonymize, batch_process, decrypt, detect, encrypt, process) are descriptive and uniformly applied, making the set predictable and easy to parse.
With 8 tools, the count is reasonable for a PII processing domain, covering detection, encryption, decryption, anonymization, and batch operations. It's slightly heavy due to overlapping tools but still within a well-scoped range for the purpose.
The toolset covers core PII operations well: detection, encryption, decryption, and anonymization, with batch processing for scalability. A minor gap exists in update or delete operations for PII data, but agents can work around this with the provided tools for most workflows.