
mcp-server-templates
Deploy Model Context Protocol servers instantly with zero configuration.
Key Features
Use Cases
README
🚀 This Project Has Moved!
⚠️ IMPORTANT: This repository has been renamed and moved to MCP Platform
What changed:
- New Repository:
Data-Everything/MCP-Platform
- New Package:
pip install mcp-platform
(replacesmcp-templates
)- New CLI:
mcpp
command (replacesmcpt
)- Enhanced Features: Improved architecture and expanded capabilities
Migration is easy:
bash# Uninstall old package pip uninstall mcp-templates # Install new package pip install mcp-platform # Use new command (all your configs work the same!) mcpp deploy demo # instead of mcpt deploy demo
MCP Server Templates (Legacy)
⚠️ This version is in maintenance mode. Please migrate to MCP Platform for latest features and updates.
� Migrate to MCP Platform • 💬 Discord Community • � Legacy Docs
Deploy Model Context Protocol (MCP) servers in seconds, not hours.
Zero-configuration deployment of production-ready MCP servers with Docker containers, comprehensive CLI tools, and intelligent caching. Focus on AI integration, not infrastructure setup.
🚀 Quick Start
# Install MCP Templates
pip install mcp-templates
# List available templates
mcpt list
# Deploy instantly
mcpt deploy demo
# View deployment
mcpt logs demo
That's it! Your MCP server is running at http://localhost:8080
⚡ Why MCP Templates?
Traditional MCP Setup | With MCP Templates |
---|---|
❌ Complex configuration | ✅ One-command deployment |
❌ Docker expertise required | ✅ Zero configuration needed |
❌ Manual tool discovery | ✅ Automatic detection |
❌ Environment setup headaches | ✅ Pre-built containers |
Perfect for: AI developers, data scientists, DevOps teams building with MCP.
🌟 Key Features
🖱️ One-Click Deployment
Deploy MCP servers instantly with pre-built templates—no Docker knowledge required.
🔍 Smart Tool Discovery
Automatically finds and showcases every tool your server offers.
🧠 Intelligent Caching
6-hour template caching with automatic invalidation for lightning-fast operations.
💻 Powerful CLI
Comprehensive command-line interface for deployment, management, and tool execution.
🛠️ Flexible Configuration
Configure via JSON, YAML, environment variables, CLI options, or override parameters.
📦 Growing Template Library
Ready-to-use templates for common use cases: filesystem, databases, APIs, and more.
📚 Installation
PyPI (Recommended)
pip install mcp-templates
Docker
docker run --privileged -it dataeverything/mcp-server-templates:latest deploy demo
From Source
git clone https://github.com/DataEverything/mcp-server-templates.git
cd mcp-server-templates
pip install -r requirements.txt
🎯 Common Use Cases
Deploy with Custom Configuration
# Basic deployment
mcpt deploy filesystem --config allowed_dirs="/path/to/data"
# Advanced overrides
mcpt deploy demo --override metadata__version=2.0 --transport http
Manage Deployments
# List all deployments
mcpt list --deployed
# Stop a deployment
mcpt stop demo
# View logs
mcpt logs demo --follow
Template Development
# Create new template
mcpt create my-template
# Test locally
mcpt deploy my-template --backend mock
🏗️ Architecture
┌─────────────┐ ┌───────────────────┐ ┌─────────────────────┐
│ CLI Tool │───▶│ DeploymentManager │───▶│ Backend (Docker) │
│ (mcpt) │ │ │ │ │
└─────────────┘ └───────────────────┘ └─────────────────────┘
│ │ │
▼ ▼ ▼
┌─────────────┐ ┌───────────────────┐ ┌─────────────────────┐
│ Template │ │ CacheManager │ │ Container Instance │
│ Discovery │ │ (6hr TTL) │ │ │
└─────────────┘ └───────────────────┘ └─────────────────────┘
Configuration Flow: Template Defaults → Config File → CLI Options → Environment Variables
📦 Available Templates
Template | Description | Transport | Use Case |
---|---|---|---|
demo | Hello world MCP server | HTTP, stdio | Testing & learning |
filesystem | Secure file operations | stdio | File management |
gitlab | GitLab API integration | stdio | CI/CD workflows |
github | GitHub API integration | stdio | Development workflows |
zendesk | Customer support tools | HTTP, stdio | Support automation |
🛠️ Configuration Examples
Basic Configuration
mcpt deploy filesystem --config allowed_dirs="/home/user/data"
Advanced Configuration
mcpt deploy gitlab \
--config gitlab_token="$GITLAB_TOKEN" \
--config read_only_mode=true \
--override metadata__version=1.2.0 \
--transport stdio
Configuration File
{
"allowed_dirs": "/home/user/projects",
"log_level": "DEBUG",
"security": {
"read_only": false,
"max_file_size": "100MB"
}
}
mcpt deploy filesystem --config-file myconfig.json
🔧 Template Development
Creating Templates
-
Use the generator:
bashmcpt create my-template
-
Define template.json:
json{ "name": "My Template", "description": "Custom MCP server", "docker_image": "my-org/my-mcp-server", "transport": { "default": "stdio", "supported": ["stdio", "http"] }, "config_schema": { "type": "object", "properties": { "api_key": { "type": "string", "env_mapping": "API_KEY", "sensitive": true } } } }
-
Test and deploy:
bashmcpt deploy my-template --backend mock
Full template development guide →
� Migration to MCP Platform
This repository has evolved into MCP Platform with enhanced features and better architecture.
Why We Moved
- Better Naming: "MCP Platform" better reflects the comprehensive nature of the project
- Enhanced Architecture: Improved codebase structure and performance
- Expanded Features: More deployment options, better tooling, enhanced templates
- Future Growth: Better positioned for upcoming MCP ecosystem developments
What Stays the Same
- ✅ All your existing configurations work unchanged
- ✅ Same Docker images and templates
- ✅ Same deployment workflows
- ✅ Full backward compatibility during transition
Migration Steps
-
Install new package:
bashpip uninstall mcp-templates pip install mcp-platform
-
Update commands:
bash# Old command mcpt deploy demo # New command (everything else identical) mcpp deploy demo
-
Update documentation bookmarks:
- New docs: https://data-everything.github.io/MCP-Platform/
- New repository: https://github.com/Data-Everything/MCP-Platform
Support Timeline
- Current (Legacy) Package: Security updates only through 2025
- New Platform: Active development, new features, full support
- Migration Support: Available through Discord and GitHub issues
�📖 Documentation (Legacy)
- Getting Started - Installation and first deployment
- CLI Reference - Complete command documentation
- Template Guide - Creating and configuring templates
- User Guide - Advanced usage and best practices
🤝 Community
- Discord Server - Get help and discuss features
- GitHub Issues - Report bugs and request features
- Discussions - Share templates and use cases
📝 License
This project is licensed under the Elastic License 2.0.
🙏 Acknowledgments
Built with ❤️ for the MCP community. Thanks to all contributors and template creators!
Star History
Repository Owner
Organization
Repository Details
Programming Languages
Tags
Topics
Join Our Newsletter
Stay updated with the latest AI tools, news, and offers by subscribing to our weekly newsletter.
Related MCPs
Discover similar Model Context Protocol servers

awslabs/mcp
Specialized MCP servers for seamless AWS integration in AI and development environments.
AWS MCP Servers is a suite of specialized servers implementing the open Model Context Protocol (MCP) to bridge large language model (LLM) applications with AWS services, tools, and data sources. It provides a standardized way for AI assistants, IDEs, and developer tools to access up-to-date AWS documentation, perform cloud operations, and automate workflows with context-aware intelligence. Featuring a broad catalog of domain-specific servers, quick installation for popular platforms, and both local and remote deployment options, it enhances cloud-native development, infrastructure management, and workflow automation for AI-driven tools. The project includes Docker, Lambda, and direct integration instructions for environments such as Amazon Q CLI, Cursor, Windsurf, Kiro, and VS Code.
- ⭐ 6,220
- MCP
- awslabs/mcp

mcpmcp-server
Seamlessly discover, set up, and integrate MCP servers with AI clients.
mcpmcp-server enables users to discover, configure, and connect MCP servers with preferred clients, optimizing AI integration into daily workflows. It supports streamlined setup via JSON configuration, ensuring compatibility with various platforms such as Claude Desktop on macOS. The project simplifies the connection process between AI clients and remote Model Context Protocol servers. Users are directed to an associated homepage for further platform-specific guidance.
- ⭐ 17
- MCP
- glenngillen/mcpmcp-server

1mcp-app/agent
A unified server that aggregates and manages multiple Model Context Protocol servers.
1MCP Agent provides a single, unified interface that aggregates multiple Model Context Protocol (MCP) servers, enabling seamless integration and management of external tools for AI assistants. It acts as a proxy, managing server configuration, authentication, health monitoring, and dynamic server control with features like asynchronous loading, tag-based filtering, and advanced security options. Compatible with popular AI development environments, it simplifies setup by reducing redundant server instances and resource usage. Users can configure, monitor, and scale model tool integrations across various AI clients through easy CLI commands or Docker deployment.
- ⭐ 96
- MCP
- 1mcp-app/agent

OpenMCP
A standard and registry for converting web APIs into MCP servers.
OpenMCP defines a standard for converting various web APIs into servers compatible with the Model Context Protocol (MCP), enabling efficient, token-aware communication with client LLMs. It also provides an open-source registry of compliant servers, allowing clients to access a wide array of external services. The platform supports integration with local and remote hosting environments and offers tools for configuring supported clients, such as Claude desktop and Cursor. Comprehensive guidance is offered for adapting different API formats including REST, gRPC, GraphQL, and more into MCP endpoints.
- ⭐ 252
- MCP
- wegotdocs/open-mcp

mcp
Universal remote MCP server connecting AI clients to productivity tools.
WayStation MCP acts as a remote Model Context Protocol (MCP) server, enabling seamless integration between AI clients like Claude or Cursor and a wide range of productivity applications, such as Notion, Monday, Airtable, Jira, and more. It supports multiple secure connection transports and offers both general and user-specific preauthenticated endpoints. The platform emphasizes ease of integration, OAuth2-based authentication, and broad app compatibility. Users can manage their integrations through a user dashboard, simplifying complex workflow automations for AI-powered productivity.
- ⭐ 27
- MCP
- waystation-ai/mcp

mcp-server-js
Enable secure, AI-driven process automation and code execution on YepCode via Model Context Protocol.
YepCode MCP Server acts as a Model Context Protocol (MCP) server that facilitates seamless communication between AI platforms and YepCode’s workflow automation infrastructure. It allows AI assistants and clients to execute code, manage environment variables, and interact with storage through standardized tools. The server can expose YepCode processes directly as MCP tools and supports both hosted and local installations via NPX or Docker. Enterprise-grade security and real-time interaction make it suitable for integrating advanced automation into AI-powered environments.
- ⭐ 31
- MCP
- yepcode/mcp-server-js
Didn't find tool you were looking for?