analytics-data

Word Counter

qpd-v

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

Analyze text documents, including counting words and characters, through Node.js.

github stars

10

0 commentsdiscussion

Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Privacy-focused — content not exposed to LLMsLocal file processing

best for

  • / Writers tracking document length
  • / Content editors analyzing text metrics
  • / Students checking assignment word counts

capabilities

  • / Count words in text documents
  • / Count characters including spaces
  • / Count characters excluding spaces
  • / Process local text files directly

what it does

Analyzes text files to count words and characters without exposing file content to LLMs. Processes documents locally through a simple Node.js interface.

about

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.

how to install

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.

license

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.

readme

MCP Word Counter

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.

Features

  • Count words in documents
  • Count total characters (including spaces)
  • Count characters excluding spaces
  • Process files directly without exposing content to LLMs

Installation

npm install mcp-wordcounter

Usage

As a CLI tool

npx mcp-wordcounter

In Claude Desktop

Add to your Claude Desktop configuration (claude_desktop_config.json):

{
  "mcpServers": {
    "mcp-wordcounter": {
      "command": "npx",
      "args": ["-y", "mcp-wordcounter"],
      "alwaysAllow": ["analyze_text"]
    }
  }
}

Available Tools

analyze_text

Counts words and characters in a text document.

Parameters:

  • filePath (string, required): Path to the text file to analyze

Returns:

  • Word count
  • Character count (including spaces)
  • Character count (excluding spaces)

Example response:

{
  "content": [{
    "type": "text",
    "text": "Analysis Results:
• Word count: 150
• Character count (including spaces): 842
• Character count (excluding spaces): 702"
  }]
}

Development

# 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

License

MIT License - see LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

FAQ

What is the Word Counter MCP server?
Word Counter is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Word Counter?
This profile displays 26 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.8 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

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

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

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

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ 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.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.826 reviews
  • Amelia Sanchez· Dec 16, 2024

    We wired Word Counter into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Ganesh Mohane· Dec 12, 2024

    Word Counter is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Aisha Diallo· Nov 7, 2024

    Word Counter reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Sakshi Patil· Nov 3, 2024

    Useful MCP listing: Word Counter is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Aanya Haddad· Oct 26, 2024

    Useful MCP listing: Word Counter is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Chaitanya Patil· Oct 22, 2024

    Word Counter reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Naina Park· Sep 21, 2024

    Word Counter is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Tariq Desai· Sep 17, 2024

    Strong directory entry: Word Counter surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Piyush G· Sep 13, 2024

    I recommend Word Counter for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Zara Nasser· Aug 12, 2024

    We evaluated Word Counter against two servers with overlapping tools; this profile had the clearer scope statement.

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