humanizer-ru
Related Servers
Alternatives to humanizer-ru
No user-submitted related servers found.
Related Servers
- AlicenseAqualityAmaintenanceStatistics of Russian texts as tools for LLM agents, built on the ruTS library: readability with school grade and reader age, officialese, SEO metrics, verse meter and rhyme, lexical diversity, morphology, syntax, keywords, concordance, collocations and stylometric comparison. Every value comes with a description, a scale reading and warnings when it is unreliable for the text6221 PyPIMIT
- AlicenseNot gradedqualityDmaintenanceZero-dependency stdio MCP server for token-aware prompt version diffs. Compare prompts or files and get machine-readable token delta reports with confidence labels.MIT
- FlicenseNot gradedqualityBmaintenanceEnables parsing of messy multi-turn Slack and WeChat Work troubleshooting threads into validated Q&A resolution pairs, stripping duplicate organizational knowledge and synthesizing structured Markdown SOP runbooks. Exposes this curation capability as MCP tools over JSON-RPC 2.0 stdio, with zero external dependencies.7-
- AlicenseNot gradedqualityCmaintenanceA local stdio shim for the hosted Textify MCP server that converts audio, video, images, documents, and webpages into text or Markdown. Includes tools for YouTube transcripts, audio transcription, OCR, file-to-Markdown, and webpage-to-Markdown extraction.5 npmMIT
- FlicenseNot gradedqualityCmaintenanceUnified MCP interface for Russian LLMs (GigaChat, YandexGPT, Ollama) with automatic fallback and 152-FZ compliant personal data anonymization.1-
- AlicenseAqualityCmaintenanceThree deterministic MCP tools that score text for AI-writing tells (em-dash density, hedge words, tricolons, boilerplate openers) and grade landing-page copy. No LLM, no network calls, no API key — same input always yields the same score. Published on the official MCP registry as io.github.parweb/ai-slop-checker.3MIT
TDQS
Scored across 7 tools
Tools have overlapping purposes: humanizer_clean, humanizer_markers, and humanizer_polish all deal with cleaning/normalizing text artifacts. Descriptions provide clarifications (e.g., clean for full cleanup, markers for detection of copy-paste marks, polish for typographic normalization), but the boundaries between them can still be confusing without careful reading.
All tool names follow a consistent pattern: humanizer_ followed by a singular noun or verb (clean, facts, report, detect, markers, polish, scan). The prefix is uniform, and naming is predictable.
7 tools is well within the typical 3-15 range and appears appropriate for the domain of text humanization/analysis, offering distinct functions like cleaning, fact-checking, reporting, detection, marker handling, polishing, and scanning.
The tool set covers key operations: cleaning, fact verification, reporting, detection, marker handling, typographic polishing, and scanning. However, there is no explicit tool for stylistic rewriting or authorship judgment (though these are intentionally excluded), and some functionality (e.g., marker removal) is only partially accessible via MCP. Minor gaps exist but core needs are met.