What is Mcp Server Boilerplate

mcp-server-boilerplate is a foundational template for building Model Context Protocol (MCP) servers in Python. It serves as a production-ready, extensible starting point that allows developers to rapidly create, extend, and deploy MCP servers that interact with LLMs and agentic clients.

Use cases

Use cases include building custom MCP servers for AI applications, developing tools for text generation and summarization, and creating interactive systems that require real-time data processing and response.

How to use

To use mcp-server-boilerplate, clone the repository, set up your Python environment, and customize the provided modules for tools, prompts, and resources. You can choose from multiple transport layers (like STDIO, SSE, HTTP) and start the server using the main entry point.

Key features

Key features include multi-transport support, modular design for tools and prompts, type-safe input validation using Pydantic, adherence to security best practices, and a clean architecture that promotes extensibility and maintainability.

Where to use

mcp-server-boilerplate can be used in various fields where interaction with LLMs and agentic clients is required, such as AI development, chatbot frameworks, and automated content generation systems.

MCP Base

A solid, foundational starting point for MCP projects. MCP Base is a production-ready, extensible template for building Model Context Protocol (MCP) servers in Python. Rapidly create, extend, and deploy MCP servers that expose tools, prompts, and resources to LLMs and agentic clients.

🚀 What is This?

This is a Python starter base—not a specific server implementation. It provides a modular, well-documented foundation for building your own MCP servers in Python, supporting multiple transport layers (STDIO, SSE, HTTP, etc.), and demonstrating best practices for security, extensibility, and maintainability.

🏗️ Architecture Overview

.
├── src/
│   ├── base/                # Base classes for tools, prompts, resources
│   ├── tools/               # Example tools (filesystem, API, prompt, etc.)
│   ├── resources/           # Example resources (static/dynamic)
│   ├── prompts/             # Example prompts (text generation, summarization)
│   ├── transports/          # Transport layer implementations & docs
│   │   ├── stdio/
│   │   │   └── README.md
│   │   ├── sse/
│   │   │   └── README.md
│   │   └── ...
│   ├── config.py            # Configuration and environment management
│   ├── server.py            # Server instantiation and registration
│   └── main.py              # Entrypoint: selects transport, starts server
├── tests/                   # Example tests for tools/resources
├── Dockerfile               # Containerized deployment
├── requirements.txt / pyproject.toml
├── README.md                # This file
├── CONTRIBUTING.md
└── ...

✨ Features

• Multi-Transport Support: STDIO, SSE, HTTP, and more (see /src/transports/)
• Modular Tools/Prompts/Resources: Add new features by creating a class and registering it
• Type-Safe Input Validation: Uses Pydantic for schemas
• Security Best Practices: Directory sandboxing, input validation, error handling
• Extensible & Maintainable: Clean separation of concerns, base classes, and registries
• Production-Ready: Logging, environment management, Docker support
• Comprehensive Documentation: For users and contributors

🛠️ Getting Started

1. Install Dependencies

pip install -r requirements.txt

2. Configure Environment

Copy .env.example to .env and fill in required values.

3. Run the Server

STDIO Transport:

python main.py --transport=stdio

SSE/HTTP Transport:
See /src/transports/sse/README.md and /src/transports/http/README.md for details.

🧩 Adding Tools, Prompts, and Resources

Tools

• Create a new class in /src/tools/ inheriting from BaseTool
• Implement the required methods and input schema
• Register the tool in the tool registry

Prompts

• Create a new class in /src/prompts/ inheriting from BasePrompt
• Implement the required methods and input schema
• Register the prompt in the prompt registry

Resources

• Add static or dynamic resources in /src/resources/
• Register them in the resource registry

🔌 Supported Transports

• STDIO: For CLI and agentic integration (see /src/transports/stdio/README.md)
• SSE: For server-sent events and web clients (see /src/transports/sse/README.md)
• HTTP: For RESTful or web-based integration (see /src/transports/http/README.md)

Each transport is modular and can be extended or replaced.

🛡️ Security & Best Practices

• All file and directory operations are sandboxed to allowed paths
• Input validation is enforced for all tool/resource inputs
• Error handling is consistent and user-friendly
• Sensitive configuration is managed via environment variables

🧪 Testing

• Example tests are provided in /tests/
• Use Pytest as the test runner
• See CONTRIBUTING.md for test guidelines

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines, code style, and PR process.

📚 Further Reading

• Model Context Protocol Documentation
• Official MCP Python SDK
• Reference MCP Servers Gallery
• Transport Layer Docs

📝 License

MIT License. See LICENSE for details.

💬 Community & Support

• Discord
• Reddit
• GitHub Discussions

MCP Base is the recommended starting point for all new Python MCP server projects. Fork, extend, and contribute improvements!