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fuzzy_match

Fuzzy string matching for entity resolution, dedupe and "did you mean": match one or many queries against up to 5,000 choices with RapidFuzz-style scorers (WRatio weighted blend, ratio / InDel similarity, partial_ratio, token_sort, token_set, Jaro-Winkler, normalised Levenshtein, Damerau / OSA). ...

SERVERScorpTech x402 API SOURCEhttps://scorptech.it.com/api/x402/mcp
Low RISK CLASS
Category Read
Parameters 81 required
Recommended Allowedsee the rule below
Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/com-it-scorptech-x402/fuzzy-match.md

What fuzzy_match does on ScorpTech x402 API

AI agents call fuzzy_match to retrieve information from ScorpTech x402 API without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.

ParameterTypeRequiredDescription
limit integer — Matches per query
query string — String to match
scorer string — Similarity scorer
choices array Yes Candidate strings
details boolean — Add every scorer for each match
queries array — Several queries (batch)
normalize boolean — Lowercase, fold accents, strip punctuation first
threshold number — Minimum score

Parameters from the server's own tool schema.

Why fuzzy_match is rated Low

Even though fuzzy_match only reads data, uncontrolled read access leaks sensitive information and racks up API costs: an agent caught in a retry loop can make thousands of calls a minute without anyone noticing.

Questions about fuzzy_match

What does the fuzzy_match tool do? +

Fuzzy string matching for entity resolution, dedupe and "did you mean": match one or many queries against up to 5,000 choices with RapidFuzz-style scorers (WRatio weighted blend, ratio / InDel similarity, partial_ratio, token_sort, token_set, Jaro-Winkler, normalised Levenshtein, Damerau / OSA). Default preprocessing lowercases, folds accents and strips punctuation. Returns top matches with 0-100 scores and choice index, optional per-scorer breakdown. Offline, deterministic. Price: $0.002 USDC per successful call (x402, Solana). Same tool over HTTPS: POST https://scorptech.it.com/api/x402/v1/fuzzy-match. It is categorised as a Read tool in the ScorpTech x402 API MCP Server, which means it retrieves data without modifying state.

What parameters does fuzzy_match accept? +

fuzzy_match accepts 8 parameters: limit, query, scorer, choices, details, queries, normalize, threshold. Required: choices. The full parameter table on this page comes from the server's own tool schema.

How do I enforce a policy on fuzzy_match? +

Register the ScorpTech x402 API MCP server in PolicyLayer and add a rule for fuzzy_match: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches ScorpTech x402 API. Nothing to install.

What risk level is fuzzy_match? +

fuzzy_match is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit fuzzy_match? +

Yes. Add a rate_limit block to the fuzzy_match rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.

How do I block fuzzy_match completely? +

Set action: deny in the PolicyLayer policy for fuzzy_match. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.

What MCP server provides fuzzy_match? +

fuzzy_match is provided by the ScorpTech x402 API MCP server (https://scorptech.it.com/api/x402/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

More on ScorpTech x402 API, and thousands of servers like it.

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