mcp-character-tools

A comprehensive MCP (Model Context Protocol) server providing character and text analysis tools to help LLMs work with individual characters—something they struggle with due to tokenization.

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Why This Exists

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

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``bash
npx mcp-character-tools
`

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`bash
npm install -g mcp-character-tools
mcp-character-tools
`

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`bash
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):

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

Available Tools

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#### count_letter
Count occurrences of a specific letter in text.

`
Input: { "text": "strawberry", "letter": "r" }
Output: { "count": 3, "positions": [2, 5, 8], "visual": "...", "density": "3 out of 10 (30.0%)" }
`

#### count_letters
Count multiple letters at once.

`
Input: { "text": "strawberry", "letters": ["r", "s", "e"] }
Output: { "results": [{ "letter": "r", "count": 3 }, { "letter": "s", "count": 1 }, { "letter": "e", "count": 1 }], "total_matches": 5 }
`

#### count_substring
Count occurrences of a substring/pattern.

`
Input: { "text": "banana", "substring": "ana", "overlapping": true }
Output: { "count": 2, "positions": [1, 3] }
`

#### letter_frequency
Get frequency distribution of all characters.

`
Input: { "text": "mississippi" }
Output: { "frequency": { "i": 4, "s": 4, "p": 2, "m": 1 }, "most_common": [...] }
`

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#### spell_word
Break text into individual characters with optional indices.

`
Input: { "text": "cat", "include_indices": true }
Output: { "characters": ["c", "a", "t"], "spelled_out": "0:'c', 1:'a', 2:'t'" }
`

#### char_at
Get character at a specific index (supports negative indices).

`
Input: { "text": "hello", "index": -1 }
Output: { "character": "o" }
`

#### nth_character
Get the nth character (1-based, human-friendly).

`
Input: { "text": "hello", "position": 2 }
Output: { "character": "e", "description": "The 2nd character of 'hello' is 'e'." }
`

#### word_length
Get exact length with detailed breakdown.

`
Input: { "text": "hello world" }
Output: { "length": 11, "length_without_spaces": 10, "space_count": 1, "word_count": 2 }
`

#### reverse_text
Reverse text and detect palindromes.

`
Input: { "text": "racecar" }
Output: { "reversed": "racecar", "is_palindrome": true }
`

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#### compare_texts
Compare letter frequencies between two texts.

`
Input: { "text1": "hello", "text2": "world" }
Output: { "common_characters": ["l", "o"], "unique_to_text1": ["h", "e"], "similarity_score": 25 }
`

#### analyze_sentence
Word-by-word breakdown for a specific letter.

`
Input: { "text": "The strawberry was very ripe", "letter": "r" }
Output: { "words": [{ "word": "The", "letter_count": 0 }, { "word": "strawberry", "letter_count": 3 }, ...], "total_count": 5 }
`

#### batch_count
Count a letter across multiple words at once.

`
Input: { "words": ["strawberry", "raspberry", "blueberry"], "letter": "r" }
Output: { "results": [{ "word": "strawberry", "count": 3 }, ...], "total_count": 6 }
`

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#### get_tricky_words
Get list of commonly miscounted words.

`
Output: List of words like "strawberry", "mississippi", "occurrence" with correct counts and explanations of common mistakes.
`

#### check_tricky_word
Check if a word is commonly miscounted.

`
Input: { "word": "strawberry" }
Output: { "is_tricky": true, "entries": [{ "letter": "r", "count": 3, "common_mistake": 2, "explanation": "..." }] }
`

All Tools Reference

| 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

`bash


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:

`bash
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