MCP Server Template

Build Model Context Protocol servers that extend AI agent capabilities with specialized tools, resources, and integrations.

Welcome to the MCP Server Developer documentation!

This section provides everything you need to design, develop, deploy, and maintain Model Context Protocol (MCP) servers. Whether you’re building your first MCP server or deploying enterprise-ready solutions, you’ll find comprehensive guidance here.

Key Areas

Quick Start - Get your first MCP server running in minutes using the enterprise template
Taking Your MCP Server to Production - Deploy, monitor, and scale your MCP server
Development Best Practices - Proven patterns for maintainable, secure MCP servers
Enterprise MCP Template - Complete development tutorial and template documentation

What is an MCP Server?

An MCP server is a specialized service that exposes tools, resources, and prompts to AI agents through the standardized Model Context Protocol. It acts as a bridge between AI agents and external systems, enabling agents to:

Execute functions (tools) like database queries, API calls, or file operations
Access data (resources) from files, databases, or external systems
Use templates (prompts) for consistent AI interactions

Key Benefits

Standardized Integration: Consistent interface for all AI agent interactions
Language Agnostic: Build servers in Python, TypeScript, Rust, or any language
Secure: Built-in authentication, authorization, and access controls
Scalable: Handle multiple concurrent agent connections efficiently
Reusable: One MCP server can serve multiple AI agents and applications

MCP Server Architecture

Info Info

Logical Architecture: This diagram shows the logical MCP protocol architecture with separate Tools, Resources, and Prompts registries. This remains accurate regardless of implementation approach - whether you implement each registry separately or use our tools-first pattern where resources and prompts are implemented as tools for universal client compatibility.

graph TB A[AI Agent] --> B[MCP Client] B --> C[MCP Protocol] C --> D[MCP Server] D --> E[Tools Registry] D --> F[Resources Registry] D --> G[Prompts Registry] E --> H[Database Tool] E --> I[API Tool] E --> J[File Tool] F --> K[Configuration Files] F --> L[Data Assets] G --> M[Query Templates] G --> N[Review Prompts] H --> O[External Database] I --> P[External API] J --> Q[File System] style D fill:#e1f5fe style E fill:#f3e5f5 style F fill:#e8f5e8 style G fill:#fff3e0

Tip Tip

Implementation Note: Our Enterprise MCP Template uses a tools-first architecture where traditional resources and prompts are implemented as tools. This provides universal client compatibility while maintaining the same logical architecture shown above.

Platform Integration

The AI Templates ecosystem provides infrastructure for MCP server development and deployment:

Development & Testing

Local Development: Modern Python tooling with UV package manager
Enterprise Template: Production-ready boilerplate with Kubernetes deployment
Comprehensive Testing: Unit tests, integration tests, and container validation
Quality Assurance: Pre-commit hooks, linting, and automated CI/CD pipelines

Production Deployment

Kubernetes Platform: Container orchestration with enterprise security
SSL/TLS: Automatic certificate management and secure communications
Load Balancing: High-availability deployment with automatic scaling
Health Monitoring: Liveness and readiness probes with metrics collection

Integration Ecosystem

External APIs: Integration with web services and data sources
Databases: Direct database connectivity for data operations
Source Control: Git integration and CI/CD automation
Observability: Performance monitoring for AI operations

Development Approaches

Learning Path (Recommended for New Developers)

Start with Quick Start - Get your first MCP server running in minutes
Apply Best Practices - Learn proven patterns and security guidelines
Deploy with Taking Your MCP Server to Production - Move to production
Deep Dive with Enterprise MCP Template - Master the fundamentals

Fast Track (Experienced Developers)

Use the Enterprise MCP Template - Start with production-ready foundation (accessed via Quick Getting Started)
Customize Tools - Implement your specific business logic using tools-first architecture
Deploy to Kubernetes - Use included deployment manifests
Monitor & Scale - Leverage built-in observability and scaling

Target Use Cases

MCP servers excel at providing AI agents with:

Data Access & Analytics

Database querying and data retrieval
Real-time analytics and reporting
Data validation and quality checks

System Integration

API orchestration and workflow automation
File system operations and content management
Configuration management and deployment automation

Domain Expertise

Business rule enforcement and validation
Specialized calculations and processing
Industry-specific knowledge and templates

Overview

As an MCP Server Developer, you will typically be involved in:

Tool Development: Creating custom tools that expose specific functionality to AI agents
Resource Management: Implementing secure access to data sources, files, and external systems
Prompt Engineering: Designing reusable prompt templates for consistent AI interactions
Architecture Design: Structuring servers for scalability, security, and maintainability
Production Deployment: Using enterprise templates and Kubernetes for reliable production hosting
Performance Optimization: Implementing caching, connection pooling, and efficient data access patterns
Security Implementation: Ensuring proper authentication, authorization, and data protection

Getting Started

Ready to build your first MCP server? Choose your path: