Japanese Text Analyzer

MCP server for analyzing Japanese text with morphological analysis
  • typescript

4

GitHub Stars

typescript

Language

6 months ago

First Indexed

2 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs
{
  "mcpServers": {
    "mistizz-mcp-japanesetextanalyzer": {
      "command": "npx",
      "args": [
        "-y",
        "github:Mistizz/mcp-JapaneseTextAnalyzer"
      ]
    }
  }
}

You can run the Japanese Text Analyzer MCP Server to perform detailed linguistic analysis on Japanese text, count characters and words, and receive insights that help guide text generation and evaluation. It supports both direct text input and file-based analysis, and it works through a lightweight MCP client workflow so you can integrate it with your preferred editor or assistant.

How to use

Use your MCP client to send text or specify a file for analysis. For quick local testing, run the server as a stdio MCP server via your MCP client configuration. You can analyze raw text to get character counts, word counts, and detailed linguistic features, or analyze a file to obtain the same metrics with file-level context.

How to install

Prerequisites you need installed on your system before running the server:

  • Node.js is required to run the MCP server through npx.
  • npm comes with Node.js and will be used to install and execute the server.
  • A working internet connection to fetch packages from the npm registry.

Step-by-step commands to set up and run the server locally via MCP tooling:

npx -y @smithery/cli install @Mistizz/mcp-JapaneseTextAnalyzer --client claude
npx -y github:Mistizz/mcp-JapaneseTextAnalyzer

Configuration and usage notes

Configure your MCP client to connect to the Japanese Text Analyzer as a stdio server using the following setup. This lets you run the analyzer as a local command that your client can invoke.

{
  "mcpServers": {
    "JapaneseTextAnalyzer": {
      "command": "npx",
      "args": [
        "-y",
        "github:Mistizz/mcp-JapaneseTextAnalyzer"
      ]
    }
  }
}

Claude for Desktop integration

To use with Claude for Desktop, add a configuration entry that runs the MCP via npx.

{
  "mcpServers": {
    "JapaneseTextAnalyzer": {
      "command": "npx",
      "args": [
        "-y",
        "github:Mistizz/mcp-JapaneseTextAnalyzer"
      ]
    }
  }
}

Cursor integration

If you use Cursor, place a similar configuration in the .cursor/mcp.json file to enable the MCP server.

{
  "mcpServers": {
    "JapaneseTextAnalyzer": {
      "command": "npx",
      "args": [
        "-y",
        "github:Mistizz/mcp-JapaneseTextAnalyzer"
      ]
    }
  }
}

Usage examples and tips

Direct text character count example and file-based word count examples are shown in practical commands. You can paste text to count characters or run the analyzer against a file to obtain both counts and language features.

Language features and tools

The analyzer provides several tools that you can invoke via the MCP interface to measure and inspect Japanese text. These include counting characters, counting words, and performing a detailed linguistic analysis that covers sentence length, part-of-speech distribution, vocabulary diversity, and other stylistic metrics.

Available tools

count_chars

Counts the number of characters in a file or text, excluding spaces and newlines.

count_words

Counts words in a file or text; Japanese uses morphological analysis, English uses spaces to separate words.

count_clipboard_chars

Counts characters in a given text input, excluding spaces and newlines.

count_clipboard_words

Counts words in a given text input; Japanese uses morphological analysis.

analyze_text

Performs detailed linguistic analysis on a text, returning metrics like average sentence length, POS distribution, and vocabulary diversity.

analyze_file

Performs detailed linguistic analysis on a file, returning aggregated metrics for the entire document.

Built by
VeilStrat
AI signals for GTM teams
© 2026 VeilStrat. All rights reserved.All systems operational