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MCP Server Template: Accelerated Development & Seamless Integration - MCP Implementation

MCP Server Template: Accelerated Development & Seamless Integration

Accelerate AI-driven MCP server development with this streamlined Python template, simplifying creation of efficient tools for seamless integration and smarter workflows.

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About MCP Server Template

What is MCP Server Template: Accelerated Development & Seamless Integration?

Imagine a Swiss Army knife for AI-driven tool development—this template slashes boilerplate code and streamlines Model Context Protocol (MCP) server creation. Designed as a zero-friction foundation, it embeds MCP specs, SDK docs, and best practices to turn your ideas into production-ready tools faster than a sprinting cheetah.

Key Features of MCP Server Template: Accelerated Development & Seamless Integration?

  • Instant server setup: Battle-tested core implementation with stdio/SSE transport toggles
  • AI-driven clarity: Embedded MCP specs (7000+ lines) and SDK guides act as contextual Rosetta Stones for AI coders
  • Clean code guardians: Cursor Rules enforce PEP8 compliance and anti-entropy patterns
  • Real-world starter kit: Weather service demo showcasing NWS API integration
  • Dependency diet: Lightweight stack with only 4 core packages

MCP Server Template Features

How to use MCP Server Template: Accelerated Development & Seamless Integration?

1. git clone the repo and activate the virtual environment
2. Customize tools via the @mcp.tool() decorator pattern
3. Choose your transport (stdio for CLI, SSE for web)
4. Scale with embedded docs guiding AI/developer collaboration

    # Launch weather demo
    python server.py --transport stdio
    

Use cases of MCP Server Template: Accelerated Development & Seamless Integration?

  • Building AI-powered CLI tools with contextual API access
  • Creating real-time data pipelines for generative models
  • Collaborative tool development where humans and AI co-edit
  • Enterprise integration of proprietary APIs with MCP compliance
  • Rapid prototyping of multi-resource tool ecosystems

MCP Server Template FAQ

FAQ from MCP Server Template: Accelerated Development & Seamless Integration?

Q: Does this require Python 3.12 magic?
A: Yes, the latest type hints and async features are mandatory for the spell to work

Q: Can I use custom transports?
A: Extend the transport.py module like a true hacker – the sky's the limit

Q: Where's the secret sauce documentation?
A: Buried in mcp.md – treat yourself to a deep dive

Q: Why Cursor Rules over plain linting?
A: They're like training wheels for AI coders, enforcing patterns that humans & models grok

Q: How do I contribute?
A: Fork, branch, and PR with the energy of a caffeine-fueled squirrel – we ❤️ improvements

Content

MCP Server template for better AI Coding

Inspired by MCP Official Tutorial

Overview

This template provides a streamlined foundation for building Model Context Protocol (MCP) servers in Python. It's designed to make AI-assisted development of MCP tools easier and more efficient.

Features

  • Ready-to-use MCP server implementation
  • Configurable transport modes (stdio, SSE)
  • Example weather service integration (NWS API)
  • Clean, well-documented code structure
  • Minimal dependencies
  • Embedded MCP specifications and documentation for improved AI tool understanding

Cursor Rules Integration

This project uses Cursor Rules for improved AI coding assistance, with patterns from Awesome Cursor Rules.

  • Clean Code Guidelines : Built-in clean code rules help maintain consistency and quality
  • Enhanced AI Understanding : Rules provide context that helps AI assistants generate better code
  • Standardized Patterns : Follow established best practices for MCP server implementation

Cursor Rules help both AI coding assistants and human developers maintain high code quality standards and follow best practices.

Integrated MCP Documentation

This template includes comprehensive MCP documentation directly in the project:

  • Complete MCP Specification (protocals/mcp.md): The full Model Context Protocol specification that defines how AI models can interact with external tools and resources. This helps AI assistants understand MCP concepts and implementation details without requiring external references.

  • Python SDK Guide (protocals/sdk.md): Detailed documentation for the MCP Python SDK, making it easier for AI tools to provide accurate code suggestions and understand the library's capabilities.

  • Example Implementation (protocals/example_weather.py): A practical weather service implementation demonstrating real-world MCP server patterns and best practices.

Having these resources embedded in the project enables AI coding assistants to better understand MCP concepts and provide more accurate, contextually relevant suggestions during development.

Requirements

  • Python 3.12+
  • Dependencies:
    • mcp>=1.4.1
    • httpx>=0.28.1
    • starlette>=0.46.1
    • uvicorn>=0.34.0

Getting Started

Installation

  1. Clone this repository:

    git clone https://github.com/yourusername/mcp-server-python-template.git

cd mcp-server-python-template
  1. Create a virtual environment and install dependencies:

    python -m venv .venv

source .venv/bin/activate  # On Windows: .venv\Scripts\activate
pip install -e .

Running the Example Server

The template includes a weather service example that demonstrates how to build MCP tools:

# Run with stdio transport (for CLI tools)
python server.py --transport stdio

# Run with SSE transport (for web applications)
python server.py --transport sse --host 0.0.0.0 --port 8080

Creating Your Own MCP Tools

To create your own MCP tools:

  1. Import the necessary components from mcp:

    from mcp.server.fastmcp import FastMCP

  2. Initialize your MCP server with a namespace:

    mcp = FastMCP("your-namespace")

  3. Define your tools using the @mcp.tool() decorator:

    @mcp.tool()

async def your_tool_function(param1: str, param2: int) -> str:
    """
    Your tool description.
    
    Args:
        param1: Description of param1
        param2: Description of param2
        
    Returns:
        The result of your tool
    """
    # Your implementation here
    return result
  1. Run your server using the appropriate transport:

    mcp.run(transport='stdio') # or set up SSE as shown in server.py

Project Structure

  • server.py: Main MCP server implementation with example weather tools
  • main.py: Simple entry point for custom code
  • protocals/: Documentation and example protocols
    • mcp.md: Complete MCP specification (~7000 lines)
    • sdk.md: MCP Python SDK documentation
    • example_weather.py: Example weather service implementation
  • pyproject.toml: Project dependencies and metadata

Understanding MCP

The Model Context Protocol (MCP) is a standardized way for AI models to interact with external tools and resources. Key concepts include:

  • Tools : Functions that models can call to perform actions or retrieve information
  • Resources : External data sources that models can reference
  • Transports : Communication channels between clients and MCP servers (stdio, SSE)
  • Namespaces : Logical groupings of related tools

This template is specifically designed to make working with MCP more accessible, with the integrated documentation helping AI tools better understand and generate appropriate code for MCP implementations.

Learning Resources

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

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

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