Essay Stats MCP
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- AlicenseNot gradedqualityBmaintenanceProvides text statistics and readability scores (Flesch Reading Ease, Flesch-Kincaid Grade Level) via offline, keyless tools.191 npmMIT
- 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.-
- AlicenseAqualityAmaintenanceProvides local, deterministic scoring and detailed auditing of text for AI-writing patterns via two read-only tools, with no network calls or language models.225 npm3MIT
- AlicenseAqualityCmaintenanceEnables users to check AI-assisted drafts for writing-pattern findings, locally clean selected hidden Unicode and lookalike characters, and optionally request API-key-authorized rewrites.3MIT
- FlicenseNot gradedqualityAmaintenanceEnables agents to analyze inline prose or files on disk for formulaic AI writing patterns, returning a 0-100 score, band label, specific violations with context, and concrete advice. Uses purely programmatic, configurable rules with no LLM judge or API calls.-
- FlicenseNot gradedqualityAmaintenanceEnables agents to analyze text strings or files for formulaic AI writing patterns, returning a 0–100 score, band label, specific violations with context, and concrete advice. It performs this rule-based linting locally without LLM judges or API calls.-
TDQS
Scored across 5 tools
Each tool targets a distinct analysis type (counting, readability formulas, style checks, limit comparison, frequency), so selection is mostly clear. The one overlap is count_text vs check_word_limit, since both report word counts; the descriptions distinguish them by the limit/exclusion features, but an agent could still pick the wrong one for a simple count.
All names use consistent snake_case, which is good. However, the conventions are mixed: count_text and check_word_limit are verb-led, while readability_scores, style_flags and word_frequency are bare noun phrases, so there is no single predictable pattern to infer a new tool's name from.
Five tools is well-scoped for an essay analysis server, and each covers a genuinely different analytical output (counts/pages, readability grades, style issues, word limits, frequency). No tool feels redundant or like padding.
The surface covers the main essay-statistics workflows: length, page estimation, readability, style flags, limit checks and repetition. Minor gaps remain, such as no grammar/spelling check, sentence-length distribution, or citation handling beyond the references exclusion option, but agents can work around these.