🛠️ Local Utilities MCP Server

A comprehensive Model Context Protocol (MCP) server built with FastMCP that provides essential utility tools for text processing, file operations, system tasks, and more.

🌟 Features

🌡️ Temperature Conversion

• Convert between Celsius and Fahrenheit

• High precision calculations with 2 decimal places

📁 File Operations

• Read text files with full content display

• Write content to files with automatic directory creation

• List directory contents with file sizes and icons

🔐 Security & Hashing

• Calculate MD5, SHA1, SHA256 hashes

• Hash text strings or entire file contents

• Base64 encoding and decoding

📊 Text Analysis

• Count words, characters, and lines

• Calculate detailed text statistics

• Average words per line and characters per word

🕒 Date & Time

• Get current date and time information

• Custom formatting with Python strftime

• Detailed breakdown (year, month, day, weekday, etc.)

🔑 Password Generation

• Generate secure passwords with customizable options

• Control length and character sets

• Include/exclude uppercase, lowercase, numbers, symbols

🚀 Quick Start

Prerequisites

• Python 3.8 or higher

• Compatible MCP client (LM Studio, Claude Desktop, etc.)

Installation

1. Clone the repository:

   git clone https://github.com/aiforhumans/local-utils-mcp.git cd local-utils-mcp

2. Create and activate a virtual environment:

   python -m venv .venv # Windows .venv\Scripts\activate # macOS/Linux source .venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

Running the Server

Start the MCP server:

The server runs on stdio transport by default, which is compatible with most MCP clients.

🔧 Configuration

LM Studio Integration

Add this configuration to your LM Studio mcp.json file:

Claude Desktop Integration

Add to your Claude Desktop configuration:

📖 API Reference

Available Tools

convert_temp(value: float, unit: str)

Convert temperature between Celsius and Fahrenheit.

Parameters:

• value: Temperature value to convert

• unit: "C" for Celsius to Fahrenheit, "F" for Fahrenheit to Celsius

Example:

read_file(file_path: str)

Read and return the contents of a text file.

Parameters:

• file_path: Absolute or relative path to the file

Returns: File contents with path information

write_file(file_path: str, content: str)

Write content to a text file with automatic directory creation.

Parameters:

• file_path: Path where to write the file

• content: Text content to write

list_directory(directory_path: str = ".")

List contents of a directory with file sizes.

Parameters:

• directory_path: Path to directory (defaults to current directory)

Returns: Formatted list with file/folder icons and sizes

calculate_hash(text_or_path: str, hash_type: str = "sha256", is_file: bool = False)

Calculate cryptographic hash of text or file content.

Parameters:

• text_or_path: Text string or file path to hash

• hash_type: "md5", "sha1", or "sha256" (default)

• is_file: Set to true when hashing a file

base64_encode_decode(text: str, operation: str = "encode")

Encode or decode text using Base64.

Parameters:

• text: Text to encode/decode

• operation: "encode" or "decode"

get_datetime_info(format_string: str = "%Y-%m-%d %H:%M:%S")

Get comprehensive current date and time information.

Parameters:

• format_string: Python strftime format string for custom formatting

text_stats(text: str)

Calculate detailed statistics for the given text.

Parameters:

• text: Text to analyze

Returns: Lines, words, characters, and averages

generate_password(length: int = 12, include_uppercase: bool = True, include_lowercase: bool = True, include_numbers: bool = True, include_symbols: bool = False)

Generate a secure random password.

Parameters:

• length: Password length (default: 12)

• include_uppercase: Include A-Z (default: true)

• include_lowercase: Include a-z (default: true)

• include_numbers: Include 0-9 (default: true)

• include_symbols: Include special characters (default: false)

🧪 Testing

Run the test suite to verify functionality:

This will test all core functions and verify the server can be imported correctly.

🔨 Development

Adding New Tools

To extend the server with additional tools:

1. Create a new function with the @mcp.tool() decorator:

2. Add proper error handling and meaningful return messages

3. Update the README with documentation for your new tool

4. Test your tool to ensure it works correctly

Project Structure

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

1. Fork the repository

2. Create your feature branch (git checkout -b feature/AmazingFeature)

3. Commit your changes (git commit -m 'Add some AmazingFeature')

4. Push to the branch (git push origin feature/AmazingFeature)

5. Open a Pull Request

📋 Requirements

• Python 3.8+

• FastMCP 2.9.0+

• MCP 1.9.4+

See requirements.txt for complete dependency list.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🔗 Related Projects

• FastMCP - The FastMCP framework used to build this server

• Model Context Protocol - Official MCP documentation

• LM Studio - Popular MCP client for local AI models

⭐ Support

If you find this project helpful, please consider giving it a star on GitHub!

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