Japanese Text Analyzer MCP Server

The Japanese Text Analyzer MCP Server fills a niche that many AI‑assistant developers overlook: the ability to perform linguistically rich analysis of Japanese text directly within an MCP workflow. By exposing a set of tools that count characters, words, and even perform full morphological analysis, the server lets developers add real‑time feedback on text quality without leaving their preferred AI environment. This is especially valuable for content creators, educators, and researchers who need to ensure that generated Japanese prose meets specific stylistic or readability standards.

At its core, the server provides two classes of functionality. First, simple metrics—character and word counts—are available for both files and clipboard text, with optional language selection. These metrics are computed by stripping whitespace or, in the case of Japanese, using a morphological tokenizer to treat each token as a word. Second, the and tools deliver a deeper dive: they report total characters, sentence counts, token totals, average sentence length, part‑of‑speech distributions, character type ratios, and lexical diversity. These insights allow an AI assistant to gauge the complexity of a paragraph or entire document, flaging overly long sentences or limited vocabulary that might hinder readability.

For developers, integration is straightforward. The MCP server can be installed via Smithery or executed directly with , and it registers itself automatically in common desktop clients such as Claude for Desktop or Cursor. Once registered, a model can invoke the tools by name, passing either file paths (absolute Windows or WSL/Linux formats) or raw text. The server’s flexible path resolution—supporting absolute, relative, and filename‑only searches—means that developers can reference local resources without hardcoding paths.

Real‑world use cases abound. A novelist using an AI assistant can request the server to analyze a draft chapter, receiving instant feedback on sentence length and vocabulary richness. An educator might ask the assistant to evaluate a student’s essay for linguistic complexity before grading. A translator could compare source and target texts, ensuring that the Japanese output maintains a comparable token distribution to the original. Even chat‑based assistants can offer on‑the‑fly character limits for social media posts or marketing copy, keeping content within platform constraints.

What sets this MCP server apart is its focus on Japanese morphology—a language where token boundaries are not whitespace‑delimited. By leveraging a robust tokenizer, the server delivers accurate word counts and part‑of‑speech breakdowns that would otherwise require external libraries. Coupled with its seamless MCP integration, developers gain a powerful, language‑specific analysis tool that can be invoked from any AI assistant supporting MCP, enabling smarter, data‑driven text generation and editing workflows.