ruTS-mcp
OfficialRelated Servers
Alternatives to ruTS-mcp
- AlicenseNot gradedqualityBmaintenanceProvides text statistics and readability scores (Flesch Reading Ease, Flesch-Kincaid Grade Level) via offline, keyless tools.3 npmMIT
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- AlicenseAqualityAmaintenanceRussian-text AI-trace detection and typography normalization over stdio MCP: finds chatbot copy-paste artifacts and invisible marks, counts soft AI-style signals; four tools, stdlib-only, no keys required.7134MIT
- FlicenseNot gradedqualityAmaintenanceScores prose from inline text or files on a 0–100 scale for formulaic AI writing patterns using rule-based heuristics, with no LLM judge or API calls. Returns band labels, specific violations with context, penalty breakdowns, and concrete advice for each hit, so agents can flag and revise sloppy generated text.-
- AlicenseNot gradedqualityCmaintenanceEnables AI assistants to score English text readability with Flesch, Flesch-Kincaid, Gunning Fog, SMOG, and text statistics, without storing the submitted text.MIT
- FlicenseNot gradedqualityAmaintenanceEnables agents to analyze text or files and receive numeric slop scores, band labels, specific violations with context, and concrete writing advice, using rule-based diagnostics with no LLM judge or API calls.-
- FlicenseNot gradedqualityAmaintenanceEnables agents to score prose from 0-100 for formulaic AI writing tells using purely programmatic rule-based heuristics, with no LLM judge or API calls. Returns a numeric score, band label, specific violations with surrounding context, per-category counts, and concrete advice, and can also analyze a file path directly.-
- FlicenseNot gradedqualityAmaintenanceScores prose from 0 to 100 for formulaic AI writing patterns using purely programmatic rules, with no LLM judge or API calls. Enables agents to lint text or files and receive a score, band label, specific violations with surrounding context, and concrete advice for each hit.-
TDQS
Scored across 6 tools
Each tool targets a clearly distinct analytical operation: collocations, general statistics, concordance, dispersion, keyness, and corpus comparison. The descriptions explicitly separate analyze_text from keyness and compare_texts, so an agent can select the right tool without overlap confusion.
The names mix noun-only labels (collocations, dispersion, keyness), verb_noun patterns (analyze_text, compare_texts), and an acronym (kwic). They remain readable and domain-appropriate, but there is no predictable convention across the set.
Six tools is well within the appropriate range and each tool earns its place by covering a distinct text-analytic operation. The broad analyze_text tool aggregates many statistic groups internally rather than inflating the external tool count.
The surface covers most major ruTS analyses, including general statistics, collocations, concordance, dispersion, keyness, and corpus comparison. A minor gap is the lack of a tool for downloading or managing the dictionaries/models that several tools require, though the descriptions point to an external setup command.