MCP For Claude Marketplace
Fuzzy Match Search

Fuzzy Match Search MCP Connector for Claude

F

Perform lightning-fast fuzzy string matching across large datasets. Find the closest matches instantly using Levenshtein distance.

1 tools Official Updated Jun 28, 2026 Official Vinkius Partner
More Details Connect to Claude

Asking an LLM to find the closest match to a misspelled name in an array of 5,000 customers consumes thousands of expensive tokens and takes seconds to process. This MCP brings ultra-fast fuzzysort algorithms to the edge, scoring and sorting targets instantly without eating your token budget.

The Superpowers

  • Zero Token Waste: Offload array searching from the LLM to the native V8 runtime.
  • Typo Tolerance: Easily finds 'Jonnathon' when the target array contains 'Jonathan'. Includes exact match highlighting.
string-matchingfuzzy-searchdata-deduplicationperformance-optimizationalgorithm

1 tools expose this connector's capabilities to your AI agent.

fuzzy_match

Pass a query and a JSON array of target strings. The engine uses fuzzy algorithms to find and rank the closest matches by similarity score. Performs lightning-fast fuzzy string matching (Levenshtein-like) across an array of targets to find the closest matches to a query

See how to talk to your AI agent using Fuzzy Match Search.

Find the closest match for 'appl' in this array of 50 fruit names.

✅ **Matches Found:** 1. Target: `Apple`, Score: `-15` 2. Target: `Pineapple`, Score: `-40`

I need the top 3 matches for 'Jonathon' from my list of 10,000 customers.

✅ **Matched:** The engine processed 10k items instantly. Best match is `Jonathan Meyers`.

Fuzzy search 'chk' against this array of bash commands.

✅ **Result:** Matches `<b>ch</b>ec<b>k</b>out` with a high score.

It uses fuzzysort, which can process 100k strings in a few milliseconds.

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