MCP Character Tools

The last thing you need for your LLM to work with individual characters or count the number of r's in a word. This is an MCP server providing 14+ comprehensive (and pretty) character and text analysis tools to help LLMs work with individual characters - something they struggle with due to tokenization.

See the Difference

<div align="center">

Without MCP (Wrong)	With MCP (Correct)
<img src="repo_assets/req_without_mcp.png" width="700" alt="Without MCP - incorrectly claims 2 r's in garlic">	<img src="repo_assets/req_with_mcp.png" width="775" alt="With MCP - correctly identifies 1 r in garlic">
Claims there are 2 r's in "garlic"	Correctly identifies 1 r in "garlic"

</div> <div align="center"> Yes, your agent will be able to tell how many r's are in Strawberry/Garlic :) </div>

Why This Exists

First of all, why not? Second, Large Language Models tokenize text into subwords, not individual characters. For example, "strawberry" might become tokens like ["straw", "berry"], so the model never truly "sees" individual letters. This MCP server gives LLMs "character-level vision" through a suite of tools.

Installation

Via npx (recommended)

npx mcp-character-tools

Via npm (global install)

npm install -g mcp-character-tools
mcp-character-tools

From source

git clone https://github.com/Aaryan-Kapoor/mcp-character-tools
cd mcp-character-tools
npm install
npm run build
npm start

Usage with Claude Desktop

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

{
  "mcpServers": {
    "char-tools": {
      "command": "npx",
      "args": ["mcp-character-tools"]
    }
  }
}

All Tools Reference

See sample_outputs.md for complete examples with inputs and outputs for all 14+ tools.

Tool	Description
count_letter	Count a specific letter
count_letters	Count multiple letters at once
count_substring	Count substring occurrences
letter_frequency	Get frequency distribution
spell_word	Break into characters
char_at	Get character at index
nth_character	Get nth character (1-based)
word_length	Get exact length
reverse_text	Reverse text, detect palindromes
compare_texts	Compare two texts
analyze_sentence	Word-by-word breakdown
batch_count	Count across multiple words
get_tricky_words	List commonly miscounted words
check_tricky_word	Check if word is tricky

Development

# Install dependencies
npm install

# Build
npm run build

# Run tests
npm test

# Run tests with coverage
npm run test:coverage

# Development mode with auto-rebuild
npm run dev

Testing

The project includes comprehensive tests for all tools:

npm test

Test files:

• tests/counting.test.ts - Counting tools tests
• tests/spelling.test.ts - Spelling tools tests
• tests/analysis.test.ts - Analysis tools tests
• tests/tricky-words.test.ts - Tricky words resource tests
• tests/visualization.test.ts - Visualization utility tests

License

MIT