readability-mcp-server
Related Servers
Alternatives to readability-mcp-server
No user-submitted related servers found.
Related Servers
- FlicenseNot gradedqualityAmaintenanceEnables agents to score prose from 0-100 for formulaic AI writing tells using pure rule-based heuristics, exposing tools to analyze inline text or files on disk and return violation details, band labels, and concrete advice without any LLM judge or network calls.-
- 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 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.-
- AlicenseNot gradedqualityAmaintenanceEnables Claude to analyze and improve readability of text using local deterministic metrics like Flesch Reading Ease and lexical diversity, without sending data to external services.1MIT
- 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.-
- AlicenseNot gradedqualityCmaintenancePublishReady helps AI agents turn drafts into cleaner, publish-ready writing using deterministic local metrics. It checks readability, structure, AI-sounding prose, revision targets, and factual preservation without external API calls.2MIT
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: one measures readability metrics, the other rewrites text to simplify it. There is no overlap or ambiguity in choosing between them.
Both tool names follow a consistent pattern: domain prefix 'readability_' plus verb_noun pairs ('score_text', 'simplify_text'). The naming is uniform and predictable.
Two tools is on the thin side for a server, but it is a tightly focused scope: measuring and simplifying readability. It feels slightly minimal but each tool earns its place.
For the stated purpose of assessing and improving plain-language readability, the server covers both key operations: evaluating a text's readability and rewriting it into plainer language. No obvious critical gap exists.