mcp-server-suite

What is mcp-server-suite?

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

How to use mcp-server-suite?

Install using uv pip install mcp-server-suite, set environment variables in a .env file, then run individual servers with commands like python web_search/web_search_sse.py. Import servers via Python, e.g., from mcp_server_suite.web_search import web_search_server.

Key features of mcp-server-suite

• 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

Use cases of mcp-server-suite

• Integrate web search capabilities using the Tavily API
• Perform safe file system operations with progress reporting
• Execute both sync and async PostgreSQL database operations
• Build AI/ML workflows with planned dataset and model servers
• Enable non-technical users to access finance, health, and task planning tools

FAQ from mcp-server-suite

What are the prerequisites for running mcp-server-suite?

Python 3.13 or higher and UV (fast Python package installer) are required. Environment variables like TAVILY_API_KEY, DATABASE_URI, and ALLOWED_BASE_PATH may be needed depending on the server.

Which servers are currently available?

Core servers include Web Search Server (Tavily), File Explorer Server, and PostgreSQL Server (sync and async). More servers are planned.

How can I contribute to mcp-server-suite?

Fork the repository, create a feature branch, add your server or improvements, and submit a pull request. See the Contributing Guidelines for details.

Where can I get support or find the community?

Support is available via the project's documentation, a Discord community, and GitHub Issues.

Are there any planned servers for specific roles?

Yes. Planned servers include Dataset Explorer, Model Evaluation, Codebase Q&A, CI/CD Monitor, Health Assistant, Finance Tracker, and many more for AI/ML engineers, software engineers, and non-technical users.