Speikzeug

Speikzeug is a modern toolset that provides MCP server integration for automated data processing and system anonymization.

Developer tools Law and compliance #Data processing #MCP integration #Anonymization #Modular .Python

rating : 2 points

downloads : 8.7K

update time : 2025-04-29

Open Site

What is Speikzeug?

Speikzeug is a modular toolset designed specifically for modern data processing needs. Through deep integration with the MCP server, it provides automated data pipeline processing capabilities. It is particularly suitable for scenarios that require handling sensitive data and ensuring compliance.

How to use Speikzeug?

After deploying through a simple installation script, configure the MCP server connection parameters to start using. The tool provides standardized data processing modules, and custom modules can also be added according to requirements.

Use cases

Suitable for scenarios such as financial data desensitization, medical record processing, and enterprise data integration that require secure and efficient processing of large amounts of data. Particularly suitable for organizations with existing MCP server infrastructure.

Main features

MCP server connection

Provides standardized API interfaces for seamless integration with existing MCP servers, supporting SSL encrypted communication and automatic retry mechanisms

Modular architecture

The extensible design allows users to add custom processing modules to meet specific business needs

Data processing pipeline

The optimized parallel processing architecture can efficiently process large-scale data sets

Automatic anonymization

Built-in data desensitization and anonymization processing functions compliant with regulations such as GDPR

Advantages

Deep integration with the MCP ecosystem

Flexible and extensible modular design

Built-in compliant data processing functions

Optimized performance for processing large-scale data

Limitations

Currently only supports the Windows platform

Requires a Python 3.11+ environment

Depends on MCP server infrastructure

How to use

Installation preparation

Ensure that the system meets the minimum requirements, including Windows 10/11 64-bit, Python 3.11+, and PowerShell 5.1+

Clone the repository

Use git to clone the project repository locally

Run the installation script

Execute the installation script to complete the environment configuration

Configure MCP connection

Edit the configuration file to set the MCP server connection parameters

Usage examples

Customer data anonymization processing

Read customer data from a CSV file, automatically desensitize sensitive information, and upload it to the MCP server

Real-time log processing

Monitor the log directory, process new log files in real-time, and send analysis results to the MCP server

Frequently Asked Questions

How to verify if the MCP server connection is successful?

Does it support Linux or MacOS systems?

How to add a custom data processing module?

Related resources

Official documentation

Complete user manual and API reference

Example code library

Configuration examples and templates for various use cases

MCP server specification

MCP server communication protocol and interface specifications

🚀 Data Storage Toolbox

A modern toolset integrated with MCP servers

This modular tool is designed for automated data processing and can be directly integrated into the MCP server infrastructure to achieve better system anonymization and scalable data management.

🚀 Quick Start

The Data Storage Toolbox is a powerful solution for integrating with MCP servers and handling data efficiently. It provides a set of features to streamline data processing and ensure compliance.

✨ Features

MCP Server Connectivity: Standardized communication interfaces for seamless integration with existing MCP environments.
Modular Design: An extensible architecture that supports user-specific custom adjustments.
Data Processing Pipeline: An optimized processing structure suitable for large-scale datasets.
Automated Anonymization: A compliance-based desensitization process.

📦 Installation

System Prerequisites

Windows 10/11 (64-bit)
Python 3.11+
MCP Server 2.4 or higher
PowerShell 5.1+

Installation Steps

# Clone the repository
git clone https://github.com/PKHexxxor/speikzeug.git
cd speikzeug

# Initialize and execute
.\tools\setup.ps1

📚 Documentation

Configuration

Configure the MCP server connection through the config\mcp_config.yaml file:

mcp:
  server:
    host: localhost
    port: 8080
    apiKey: %%MCP_API_KEY%%
  connection:
    timeout: 30
    retries: 3
    useSSL: true

Architecture Overview

speikzeug/
├── core/            # Core components
├── services/        # Service modules
├── connectors/      # MCP server connectors
├── processors/      # Data processing modules
├── utils/           # Auxiliary functions
├── config/          # Configuration files
└── tools/           # Installation tools