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mcp-server-suite

Created By
Dadiya-Harsh9 months ago
A customizable and extensible MCP (Model Context Protocol) servers designed for automation, integration, and AI-powered workflows. Features a modular architecture, easy API integration, and support for creating and managing custom tools and services
Content

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

License

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


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