MCP Server Template H61
MCP server template project, providing configuration examples for running MCP servers from GitHub, PyPI, or locally
rating : 2.5 points
downloads : 20
What is MCP Server Template?
MCP Server Template is a pre - configured server template that helps users quickly set up and run a Model Context Protocol (MCP) server. It provides standardized startup methods and configuration options.How to use MCP Server Template?
You can use this template in three ways: install directly from the GitHub repository, install via the PyPI package manager, or run from a local directory.Use Cases
Suitable for developers who need to quickly deploy MCP servers, especially team projects that want to standardize server configurations.Main Features
Multi - source InstallationSupports installation and running from three sources: GitHub, PyPI, or a local directory
uvx Tool SupportUse the uvx tool for convenient installation and management
Standardized ConfigurationProvides a standardized MCP server configuration template
Advantages and Limitations
Advantages
Rapid deployment: Server setup can be completed within minutes
Flexibility: Supports multiple installation sources
Standardization: Provides consistent server configurations
Limitations
Requires pre - installation of the uvx tool
Running locally requires a Python environment
Advanced customization requires technical knowledge
How to Use
Choose an Installation Method
Choose to run from GitHub, PyPI, or locally according to your needs
Install Dependencies
Ensure that the uvx tool and Python environment are installed
Run the Server
Execute the corresponding command according to the selected installation method
Usage Examples
Quickly Test New FeaturesDevelopers can directly install the latest version from GitHub for feature testing
Deploy a Stable VersionUse the stable version released on PyPI in the production environment
Frequently Asked Questions
What is uvx? How to install it?
How to update the server version?
What permissions are required to run locally?
Related Resources
GitHub Repository
Project source code and issue tracking
PyPI Page
Officially released Python package
MCP Protocol Documentation
Official documentation for the Model Context Protocol
Featured MCP Services

Notion Api MCP
Certified
A Python-based MCP Server that provides advanced to-do list management and content organization functions through the Notion API, enabling seamless integration between AI models and Notion.
Python
141
4.5 points

Duckduckgo MCP Server
Certified
The DuckDuckGo Search MCP Server provides web search and content scraping services for LLMs such as Claude.
Python
830
4.3 points

Markdownify MCP
Markdownify is a multi-functional file conversion service that supports converting multiple formats such as PDFs, images, audio, and web page content into Markdown format.
TypeScript
1.7K
5 points

Gitlab MCP Server
Certified
The GitLab MCP server is a project based on the Model Context Protocol that provides a comprehensive toolset for interacting with GitLab accounts, including code review, merge request management, CI/CD configuration, and other functions.
TypeScript
87
4.3 points

Figma Context MCP
Framelink Figma MCP Server is a server that provides access to Figma design data for AI programming tools (such as Cursor). By simplifying the Figma API response, it helps AI more accurately achieve one - click conversion from design to code.
TypeScript
6.7K
4.5 points

Unity
Certified
UnityMCP is a Unity editor plugin that implements the Model Context Protocol (MCP), providing seamless integration between Unity and AI assistants, including real - time state monitoring, remote command execution, and log functions.
C#
567
5 points

Minimax MCP Server
The MiniMax Model Context Protocol (MCP) is an official server that supports interaction with powerful text-to-speech, video/image generation APIs, and is suitable for various client tools such as Claude Desktop and Cursor.
Python
754
4.8 points

Context7
Context7 MCP is a service that provides real-time, version-specific documentation and code examples for AI programming assistants. It is directly integrated into prompts through the Model Context Protocol to solve the problem of LLMs using outdated information.
TypeScript
5.2K
4.7 points