Writing Tools MCP Server

This is a Model Context Protocol (MCP) server designed to provide various text analysis tools, assisting users in improving their writing. It is optimized for Claude Desktop with one-click installation via MCP bundles, and also works with other MCP-compatible tools like Cursor and Windsurf.

MCP servers act as a secure bridge or interface, enabling AI models and language assistants to interact with local applications, tools, or data on a user’s machine. This server leverages that protocol to offer its specialized writing-specific analysis capabilities to connected AI clients.

Features

This server provides the following text analysis tools:

• list_tools: List all available tools in this server.
• character_count: Return the number of characters in the input text.
• word_count: Return the number of words in the input text.
• spellcheck: Return a list of misspelled words in the input text.
• readability_score: Return readability scores (Flesch, Kincaid, Fog) for the text, section, or paragraph level. Successful responses include a findings array.
• reading_time: Return the estimated reading time for the text, section, or paragraph level.
• keyword_density: Calculate the density of a given keyword in the text. Returns {"keyword", "density", "findings"}.
• keyword_frequency: Count how often each keyword appears in the text (optionally removing stopwords). Returns {"frequencies", "findings"}.
• top_keywords: Identify the most frequently used keywords in the text. Returns {"keywords", "findings"}.
• keyword_context: Extract sentences or phrases where a specific keyword appears. Returns {"keyword", "sentences", "findings"}.
• passive_voice_detection: Detect passive voice constructions in the text.
• perplexity_analysis: Analyze text for perplexity and burstiness to detect AI-generated content using GPT-2. Successful responses include a findings array.
• stylometric_analysis: Analyze stylometric features (sentence length, length-robust lexical diversity and vocabulary rarity, POS ratios and bigrams, six readability grade-level formulas, syntactic complexity, punctuation idiosyncrasies, hedge/booster rate, per-function-word/Burrows’ Delta profile, character n-gram profile) for AI detection, against a built-in or custom baseline. Successful responses include a findings array.
• stylometric_delta: Profile a draft and its revision against the same baseline and report what the revision moved: per-statistic z-score deltas, an improved/regressed/unchanged verdict per dimension, and a findings array for the revised text.