In the era of AI-powered automation services, tools based on MCP (Multi Control Panel) have become essential. These tools go beyond simple code editors to automate entire projects and handle servers and data.
MCP (Model Context Protocol) is a powerful automation tool not only for developers but also for planners, startup teams, and AI service operators.
If you want to handle everything from content planning and writing to web and app development, analytics, APIs, and security all at once, be sure to check it out.
This article categorizes MCP tools for those seeking to build their own AI-powered automation systems and boost productivity.
✅ What you'll gain from this article:
Quickly grasp which tools are needed when building emotion data-based services
Design the entire workflow from development → data → AI → security
Recommendations for tools by use case that can be applied directly to real work
MCP Tool Overview
🗂️ Categories
MCP Tool Name
Key Feature Description
📝 Core Development Tools
text-editor MCP
Text editor functionality for direct code file modification
edit-file-lines MCP
Precise editing possible at the line level (useful for automation)
git MCP
Source code version control, branch strategy, and collaboration tracking features
📋 Project Management
shrimp task manager MCP
Individual/team task lists, schedule management, and progress tracking
🌐 Web Automation & Context Management
playwright MCP
Browser-based automation and user simulation testing
context7 MCP
Context tracking and session state management (suitable for large-scale systems)
🔧 Development Environment Management
docker MCP
Automated configuration and deployment of container-based virtual development environments
database MCP
PostgreSQL DB connection, table/query/schema management
redis MCP
Redis-based cache system management and session optimization
📊 Data Processing & Analysis
pandas MCP
Core tools for sentiment data preprocessing and statistical analysis
jupyter MCP
Notebook execution environment for data visualization and model validation
csv MCP
CSV-based sentiment log dataset processing
🔄 API Development & Testing
rest-api MCP
REST API structure design and basic call testing
postman MCP
Automated API requests, scenario-based testing enabled
swagger MCP
OpenAPI-based automatic API specification documentation
🧠 AI/ML Development
python-ml MCP
Python environment for ML model development (KoBERT, sentiment classifiers, etc.)
huggingface MCP
Transformer-based model loading and fine-tuning environment
tensorflow MCP
Deep learning-based sentiment prediction and tagging algorithm configuration
📱 Monitoring & Performance
prometheus MCP
Real-time performance metric collection and alert system implementation
log-analyzer MCP
Log-based user behavior analysis and debugging
🔐 Security & Encryption
encryption MCP
Emotional data and sensitive information encryption processing capabilities