avoid-ai-writing-mcp
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- AlicenseAqualityAmaintenanceBilingual (EN/ES) AI-writing detection that shows the evidence instead of a percentage: named tells with line and column, hidden-character inspection, and citation cross-checking against a document's own bibliography. Seven of its nine tools run entirely locally and never touch the network.1023MIT
- AlicenseAqualityBmaintenanceA private, open-source AI-text checker. Get a read on whether text looks AI-written, the exact AI-tell spans to fix, a reuse check, and a grammar pass.4MIT
- AlicenseNot gradedqualityAmaintenanceProvides agents with tools to score a page's AI readiness, check which AI crawlers robots.txt blocks, and assess passage citability—all performed locally.3702MIT
- AlicenseAqualityCmaintenanceEnables editorial content QA over the Model Context Protocol with four local tools that analyze readability, AI-sounding language, SEO on-page factors, and produce full reports where every finding includes an actionable fix.41MIT
- AlicenseAqualityDmaintenanceEnables AI attribution audits and quick transparency checks on written content, providing authorship analysis and segment breakdowns for editorial review.22MIT
- AlicenseNot gradedqualityCmaintenanceai-detect is a free AI text detector that runs entirely on your own hardware. It scores writing sentence by sentence using desklib/ai-text-detector-v1.01, a 304-million-parameter DeBERTa-v3-large model that ranks first on the RAID detection benchmark. No API key, no per-word pricing and no upload: after a one-off 1.7 GB model download it just works offline. by Houtini4MIT
TDQS
Scored across 2 tools
score_text and audit_text have clearly separated purposes: one returns a compact classification while the other returns detailed flagged patterns and highlighted regions. The cross-references in the descriptions remove any real ambiguity about which tool to use.
Both tool names follow the same verb_noun pattern with lowercase snake_case and concise action words. This is fully consistent and predictable.
Two tools is slightly below the typical 3-15 range, but for a narrowly scoped local text detector the pair covers both a compact score and a detailed audit. Each tool has a distinct, useful role, so the small count feels intentional rather than inadequate.
The server's apparent domain is deterministic AI-writing detection on text, and the two tools cover the full user journey: getting a quick judgment or drilling into specific flagged patterns and highlighted regions. There are no obvious missing operations for this stated purpose.