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). ...
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.
| Parameter | Type | Required | Description |
|---|---|---|---|
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.
Attacks that exploit this kind of access
The rule that runs fuzzy_match safely
PolicyLayer is an MCP gateway: it sits between your AI agents and ScorpTech x402 API, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For fuzzy_match, this is the rule to start with:
fuzzy_match is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect ScorpTech x402 API, apply this rule, and every fuzzy_match call is checked against it from then on.
Questions about 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). 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.
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.
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.
fuzzy_match is a Read tool with low risk. Read-only tools are generally safe to allow by default.
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.
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.
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.
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