Clean messy unicode, zero-width chars, smart quotes, and emoji from text before LLM input via @mukundakatta/textsanity-mcp (npx)

Recipe: sanitize messy text via @mukundakatta/textsanity-mcp

Server

• Package: @mukundakatta/textsanity-mcp v0.1.1
• Transport: stdio
• Launch: npx -y @mukundakatta/textsanity-mcp
• Auth: none

Tools (2)

Verified trace

sanitize — dirty web-scraped text with zero-width chars, non-breaking spaces, smart quotes, and emoji:

→ {"method":"tools/call","params":{"name":"sanitize","arguments":{"text":"Hello​ World    ‘quotes’ and “double” 😀  plus​some​zero​width","preset":"strict"}}}
← {"content":[{"type":"text","text":"{\"clean\": \"Hello World 'quotes' and \\\"double\\\" plussomezerowidth\"}"}]}

Result: zero-width chars stripped, non-breaking spaces → regular space, multiple spaces collapsed, smart quotes → ASCII, emoji removed.

normalize_newlines — mixed line endings:

→ {"method":"tools/call","params":{"name":"normalize_newlines","arguments":{"text":"line one\r\nline two\rline three\nline four"}}}
← {"content":[{"type":"text","text":"{\"clean\": \"line one\\nline two\\nline three\\nline four\"}"}]}

When to use

Before feeding web scrapes, PDF extracts, or user-pasted text into an LLM. Catches token-smuggling via zero-width chars, prevents string comparison breakage from smart quotes, and normalizes whitespace for consistent tokenization.