Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@char-index-mcpfind the 2nd occurrence of 'e' in 'hello there everyone'"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
char-index-mcp
A Model Context Protocol (MCP) server providing character-level index-based string manipulation. Perfect for test code generation where precise character positioning matters.
This project was created with Claude AI.
🎯 Why This Exists
LLMs generate text token-by-token and struggle with exact character counting. When generating test code with specific length requirements or validating string positions, you need precise index-based tools. This MCP server solves that problem.
✨ Features (12 Tools)
🔍 Character & Substring Finding (4 tools)
find_nth_char- Find nth occurrence of a characterfind_all_char_indices- Find all indices of a characterfind_nth_substring- Find nth occurrence of a substringfind_all_substring_indices- Find all occurrences of a substring
✂️ Splitting (1 tool)
split_at_indices- Split string at multiple positions
✏️ String Modification (3 tools)
insert_at_index- Insert text at specific positiondelete_range- Delete characters in rangereplace_range- Replace range with new text
🛠️ Utilities (3 tools)
find_regex_matches- Find regex pattern matches with positionsextract_between_markers- Extract text between two markerscount_chars- Character statistics (total, letters, digits, etc.)
📦 Batch Processing (1 tool)
extract_substrings- Extract one or more substrings (unified tool)
🚀 Installation
Option 1: Using uvx (Recommended)
No installation required! Just configure and run:
# Test it works
uvx char-index-mcp --helpOption 2: From PyPI
pip install char-index-mcpOption 3: From Source
git clone https://github.com/agent-hanju/char-index-mcp.git
cd char-index-mcp
pip install -e .🔧 Configuration
Claude Desktop
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Using uvx (Recommended)
{
"mcpServers": {
"char-index": {
"command": "uvx",
"args": ["char-index-mcp"]
}
}
}Using pip install
{
"mcpServers": {
"char-index": {
"command": "char-index-mcp"
}
}
}Claude Code
# Using uvx (recommended)
claude mcp add char-index '{"command":"uvx","args":["char-index-mcp"]}'
# Using pip install
claude mcp add char-index '{"command":"char-index-mcp"}'Cursor
Add to ~/.cursor/mcp.json:
Using uvx (Recommended)
{
"mcpServers": {
"char-index": {
"command": "uvx",
"args": ["char-index-mcp"]
}
}
}Using pip install
{
"mcpServers": {
"char-index": {
"command": "char-index-mcp"
}
}
}📖 Usage Examples
Finding Characters
# Find 3rd occurrence of 'l'
find_nth_char("hello world", "l", 3) # Returns: 9
# Find all occurrences of 'l'
find_all_char_indices("hello world", "l") # Returns: [2, 3, 9]Working with Substrings
# Find 2nd "hello"
find_nth_substring("hello hello world", "hello", 2) # Returns: 6
# Find all occurrences
find_all_substring_indices("hello hello world", "hello") # Returns: [0, 6]String Manipulation
# Insert comma after "hello"
insert_at_index("hello world", 5, ",") # Returns: "hello, world"
# Delete " world"
delete_range("hello world", 5, 11) # Returns: "hello"
# Replace "world" with "Python"
replace_range("hello world", 6, 11, "Python") # Returns: "hello Python"Splitting & Extracting
# Split at multiple positions
split_at_indices("hello world", [2, 5, 8]) # Returns: ["he", "llo", " wo", "rld"]
# Extract single character
extract_substrings("hello", [{"start": 1, "end": 2}])
# Returns: [{"start": 1, "end": 2, "substring": "e", "length": 1}]
# Batch extraction
extract_substrings("hello world", [
{"start": 0, "end": 5},
{"start": 6, "end": 11}
])
# Returns: [
# {"start": 0, "end": 5, "substring": "hello", "length": 5},
# {"start": 6, "end": 11, "substring": "world", "length": 5}
# ]Pattern Matching
# Find all numbers with their positions
find_regex_matches("test123abc456", r"\d+")
# Returns: [
# {"start": 4, "end": 7, "match": "123"},
# {"start": 10, "end": 13, "match": "456"}
# ]Extracting Text
# Extract content between markers
extract_between_markers("start[content]end", "[", "]", 1)
# Returns: {
# "content": "content",
# "content_start": 6,
# "content_end": 13,
# "full_start": 5,
# "full_end": 14
# }🧪 Development
# Clone the repository
git clone https://github.com/agent-hanju/char-index-mcp.git
cd char-index-mcp
# Install in development mode
pip install -e ".[dev]"
# Run tests
pytest
# Run with coverage
pytest --cov=char_index_mcp --cov-report=term-missing🎯 Use Cases
Test Code Generation: Generate strings with exact character counts
Data Processing: Split/extract data at precise positions
Text Formatting: Insert/delete/replace at specific indices
Pattern Analysis: Find and extract pattern matches with positions
LLM Response Parsing: Extract content between XML tags by position
📝 Example: Test Code Generation
# Ask Claude: "Generate a test string that's exactly 100 characters long"
# Claude can use count_chars() to verify the exact length
# Ask: "Find where the 5th comma is in this CSV line"
# Claude can use find_nth_char(csv_line, ",", 5)
# Ask: "Split this string at characters 10, 25, and 50"
# Claude can use split_at_indices(text, [10, 25, 50])
# Ask: "Extract the text between the 2nd <thinking> and </thinking> tags"
# Claude can use extract_between_markers(text, "<thinking>", "</thinking>", 2)🤝 Contributing
Contributions are welcome! Please:
Fork the repository
Create a feature branch
Add tests for new functionality
Submit a pull request
📄 License
MIT License - see LICENSE file for details
🔗 Related Projects
mcp-character-counter - Character counting & analysis
mcp-wordcounter - Word & character counting for files
text-master-mcp - Comprehensive text processing toolkit
📮 Contact
For issues, questions, or suggestions, please open an issue on GitHub.
Note: This is the first MCP server specifically designed for index-based string manipulation. All other text MCP servers focus on counting, case conversion, or encoding - not precise character positioning.