by qpd-v
Use our Word Counter to quickly count the words in Word docs. Get accurate word and character counts with this word coun
Analyzes text files to count words and characters without exposing file content to LLMs. Processes documents locally through a simple Node.js interface.
Word Counter is a community-built MCP server published by qpd-v that provides AI assistants with tools and capabilities via the Model Context Protocol. Use our Word Counter to quickly count the words in Word docs. Get accurate word and character counts with this word coun It is categorized under analytics data. This server exposes 1 tool that AI clients can invoke during conversations and coding sessions.
You can install Word Counter in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
Apache-2.0
Word Counter is released under the Apache-2.0 license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
We wired Word Counter into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Word Counter is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Word Counter reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
Useful MCP listing: Word Counter is the kind of server we cite when onboarding engineers to host + tool permissions.
Useful MCP listing: Word Counter is the kind of server we cite when onboarding engineers to host + tool permissions.
Word Counter reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
Word Counter is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
Strong directory entry: Word Counter surfaces stars and publisher context so we could sanity-check maintenance before adopting.
I recommend Word Counter for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
We evaluated Word Counter against two servers with overlapping tools; this profile had the clearer scope statement.
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A Model Context Protocol server that provides tools for analyzing text documents, including counting words and characters. This server helps LLMs perform text analysis tasks by exposing simple document statistics functionality.
npm install mcp-wordcounter
npx mcp-wordcounter
Add to your Claude Desktop configuration (claude_desktop_config.json):
{
"mcpServers": {
"mcp-wordcounter": {
"command": "npx",
"args": ["-y", "mcp-wordcounter"],
"alwaysAllow": ["analyze_text"]
}
}
}
Counts words and characters in a text document.
Parameters:
filePath (string, required): Path to the text file to analyzeReturns:
Example response:
{
"content": [{
"type": "text",
"text": "Analysis Results:
• Word count: 150
• Character count (including spaces): 842
• Character count (excluding spaces): 702"
}]
}
# Install dependencies
npm install
# Build the project
npm run build
# Run in watch mode during development
npm run watch
# Test with MCP Inspector
npm run inspector
MIT License - see LICENSE file for details.
Contributions are welcome! Please feel free to submit a Pull Request.
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.