Regex Toolkit MCP. Guarantee 100% Accurate Data Extraction & Redaction.

Listen up, 'cause most general-purpose LLMs are sloppy when it comes to pulling structured data out of a big chunk of text. They guess where an email ends or they hallucinate phone numbers when they summarize something—it's garbage. This Regex Toolkit MCP fixes that mess by forcing every single piece of data through strict mathematical patterns.

You never get guesswork here; you get pure, reliable structure.

This server gives your agent three highly specific tools for handling contact details and validation. When you need to pull all the good stuff out of a massive transcript or report, use extract_pattern. This tool doesn't just list contacts; it pulls every unique email address, URL, or phone number from whatever text block you feed it, spitting the results back as a clean, predictable JSON array.

If you dump a whole meeting transcript into this, you know exactly what you're gonna get: a structured list of all the contact patterns without missing a single one.

If your goal is to make that data public—like for a report or a knowledge base entry—and you gotta redact anything sensitive first, use mask_sensitive_data. This tool scans the entire text blob and instantly replaces every piece of PII it finds—emails, phone numbers, and URLs—with standardized [REDACTED] tags. It's quick, clean sanitization for public-facing documents.

When you need to confirm that a user input is actually in the right format before processing anything else, run validate_pattern. You feed it a single string, and this tool checks if that string perfectly matches the mathematical structure required for an email, URL, or phone number. It's a hard pass/fail check; there's no gray area here.

Think about the sheer volume of unstructured text you deal with. Whether you’re processing a batch of customer feedback containing dozens of contacts, or whether you just need to verify if that single piece of data someone typed in is actually valid—you got your answer right here. The extract_pattern tool handles entire passages and reliably pulls out all unique contact patterns into a usable JSON list.

The mask_sensitive_data tool ensures that when you're sharing data, the PII is stripped clean by replacing emails, phones, and URLs with those standard [REDACTED] tags. You use validate_pattern to make sure an input string adheres perfectly to the mathematical structure of a phone number, URL, or email before your client runs any other process on it.

These tools let you treat data validation like code—it's absolute and precise. Your agent doesn't guess; it executes based on rules. You feed extract_pattern that massive text dump, and it spits back a structured JSON array of unique emails, URLs, or phone numbers. When you need to sanitize for public consumption, mask_sensitive_data scans the whole thing and replaces all sensitive data with standardized [REDACTED] tags.

If you're just checking one piece of info—say, an email address passed in a form—you run validate_pattern, which confirms if that single input matches the perfect mathematical format for emails, URLs, or phone numbers. This server guarantees that whatever contact pattern extraction, sanitization, or validation task you give it, you're getting machine-enforced accuracy every time.