mcp-server-suite

mcp-server-suite

mcp-server-suite is a comprehensive collection of MCP (Model Context Protocol) servers designed to be the "HuggingFace for automation". Our vision is to create a community-driven platform where developers can both use pre-built servers and contribute their own, making automation and AI-powered workflows accessible to everyone.

Features

• Model Context Protocol core for flexible context management
• Modular architecture for adding new tools and services
• Growing collection of ready-to-use MCP servers
• Easy integration with APIs and external systems
• Environment variable support via .env files
• Community-driven expansion and customization

Available Servers

Core Servers

• Web Search Server: Integrate web search capabilities using Tavily API
• File Explorer Server: Safe file system operations with progress reporting
• PostgreSQL Server: Both sync and async database operations
• More coming soon!

Getting Started

Prerequisites

• Python 3.13 or higher
• UV - Fast Python package installer and resolver

Installation

1. Clone the repository:

   git clone https://github.com/Dadiya-Harsh/mcp-server-suite.git
   cd mcp-server-suite
   

2. Install UV if you haven't already:

   pip install uv
   

3. Create and activate a virtual environment:

   uv venv
   # On Windows:
   .venv\Scripts\activate
   # On Unix/macOS:
   source .venv/bin/activate
   

4. Install dependencies using UV:

   uv pip install -e .
   

5. Set up your environment variables in a .env file:

   # Required for web search server
   TAVILY_API_KEY=your_api_key_here
   
   # Required for PostgreSQL server
   DATABASE_URI=postgresql://user:password@localhost:5432
   
   # Required for file explorer server
   ALLOWED_BASE_PATH=E:\Your\Safe\Path
   

Running the Servers

Each server can be run independently:

# Web Search Server
python web_search/web_search_sse.py

# File Explorer Server
python file_explore/basic_file_server.py

# PostgreSQL Server (async)
python database/postgresql/async_postgresql_server.py

Using the Platform

As a User

1. Install the package:

   uv pip install mcp-server-suite
   

2. Import and use any server:

   from mcp_server_suite.web_search import web_search_server
   from mcp_server_suite.database import async_postgresql_server
   

📌 Planned MCP Servers

For AI/ML Engineers

• Dataset Explorer MCP: Browse/filter datasets from HuggingFace, Kaggle, etc.
• Model Evaluation MCP: Compare model performance across tasks or datasets.
• Experiment Tracker MCP: Visualize experiment logs and metrics from MLflow/W&B.
• Training Job Manager MCP: Start, monitor, and manage training jobs locally or on the cloud.
• Paper Summary MCP: Summarize and interact with AI papers via arXiv or Semantic Scholar.

For Software Engineers

• Codebase Q&A MCP: Ask natural language questions about source code.
• Issue Debugger MCP: Summarize GitHub/GitLab issues and provide solutions.
• CI/CD Monitor MCP: Check pipeline status and deployment history.
• API Inspector MCP: Query and understand OpenAPI/Swagger docs via natural prompts.

For Non-Technical Users

• Health Assistant MCP: Get simple, symptom-based answers and medication guidance.
• Finance Tracker MCP: Analyze and summarize personal spending from CSV/PDF exports.
• Smart Task Planner MCP: Convert vague goals into structured, scheduled learning/task plans.
• FAQ Assistant MCP: Answer questions based on uploaded files or organization docs.

Vision & Roadmap

We're building the "HuggingFace for automation" with these goals:

Immediate Goals

• Expand server collection with most-requested functionalities
• Build a central registry for discovering and sharing servers
• Create comprehensive documentation and examples
• Implement authentication and security features

Community Features (Coming Soon)

• Server discovery and search
• Easy server deployment and sharing
• Version control and dependency management
• Usage analytics and monitoring
• Community ratings and reviews

Server Categories

Data & Analytics

• Database connectors (MySQL, MongoDB, etc.)
• Analytics engines
• Data transformation tools

AI/ML Tools

• Model deployment servers
• Training job managers
• Dataset handlers
• Experiment trackers

DevOps & Infrastructure

• CI/CD integrators
• Cloud service managers
• Monitoring tools
• Log analyzers

Application Integration

• Email/SMS servers
• Payment processors
• Authentication services
• File storage connectors

Contributing

Join us in building the future of automation! Contributions are welcome:

1. Fork the repository
2. Create your feature branch
3. Add your server or improvements
4. Submit a pull request

See our Contributing Guidelines for more details.

Support

• Documentation
• Discord Community
• GitHub Issues

License

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

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