MCP Character Tools

# 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) ```bash npx mcp-character-tools ``` ### Via npm (global install) ```bash npm install -g mcp-character-tools mcp-character-tools ``` ### From source ```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"] } } } ``` ## All Tools Reference > ## See [sample_outputs.md](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 ```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