regexlab-mcp
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- AlicenseAqualityDmaintenanceProvides tools to test regex patterns for correctness, performance (ReDoS), and memory usage, and suggests safe rewrites. Enables LLMs to iterate on regex generation with verifiable feedback.9MIT
- AlicenseAqualityCmaintenanceRuns a regular expression on real engines — JavaScript (V8), Python (CPython re), PCRE2 and RE2 — reporting each engine's own compile errors with positions, every match with its groups and named groups, replacement results, and where the engines disagree. It also detects catastrophic backtracking (ReDoS), naming the at-risk part of the pattern, generating an attack string, and timing how long each engine actually takes on it.21MIT
- AlicenseAqualityCmaintenanceEnables AI agents to explain regex patterns in plain English and statically assess ReDoS risk before shipping, without executing the pattern or requiring an API key.2MIT
- AlicenseNot gradedqualityCmaintenanceEnables AI assistants to verify, explain, and audit regular expressions entirely offline, including detection of ReDoS vulnerabilities and compatibility across different regex engines.MIT
- AlicenseNot gradedqualityCmaintenanceProvides sandboxed code execution for AI agents with support for Python, JavaScript, and shell commands. Includes comprehensive safety features like destructive pattern blocking, timeout protection, and restricted file access for secure production use.21 npm43 PyPIMIT
- FlicenseNot gradedqualityCmaintenanceRegexForge gives AI agents a reliable way to get a regex without asking an LLM to hallucinate one. Pass in labeled examples (strings that should match, strings that shouldn't) plus an optional description; get back the regex, a proof matrix showing it handles every example, and a backtracking-risk audit flagging catastrophic-backtracking patterns. Pure symbolic synthesis over a template bank with-
TDQS
Scored across 7 tools
Each tool has a clearly stated, distinct job: testing, explaining, ReDoS analysis, cross-engine comparison, syntax conversion, substitution preview, and test generation. Minor overlap exists because test_regex already reports engine disagreements and killed timeouts, blurring its boundary slightly with compare_engines and check_backtracking, but the descriptions explicitly cross-reference when to use which.
All seven tools follow a strict snake_case verb_noun convention (test_regex, explain_regex, check_backtracking, compare_engines, convert_pattern, replace_preview, suggest_tests). The verb choice is also semantically meaningful and consistent across the set.
Seven tools is well within the ideal 3-15 range for a regex workbench and each tool covers a genuinely different stage of the regex lifecycle. Nothing appears redundant or thin.
The surface covers validation, explanation, performance safety, portability, conversion, substitution, and test generation—essentially the full regex workflow. Minor gaps remain, such as no explicit tool to list available engines/capabilities or to benchmark raw pattern throughput, but agents can work around these via test_regex output.