Enterprise MCP Python Server

Enterprise implementation of a Model Context Protocol (MCP) Server and Client using Python, demonstrating Streamable HTTP, JSON-RPC, tool discovery, and enterprise-ready architecture patterns for AI Agents.

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📖 Overview

This project demonstrates how to build a complete Model Context Protocol (MCP) solution using the official Python SDK.

The implementation includes:

• Enterprise MCP Server
• MCP Client
• Streamable HTTP Transport
• JSON-RPC Communication
• Automatic Tool Discovery
• Enterprise Cloud Architecture Services

The project simulates how AI Agents can securely communicate with enterprise systems through MCP while exposing reusable tools.

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🏗️ Architecture

                        +----------------------+
                        |      Developer       |
                        +----------+-----------+
                                   |
                                   |
                            Python MCP Client
                                   |
                     JSON-RPC / Streamable HTTP
                                   |
                        +----------v-----------+
                        | Enterprise MCP Server|
                        +----------+-----------+
                                   |
      -----------------------------------------------------------
      |                     |                     |              |
server_status()      add_numbers()     cloud_architecture()  calculate_cloud_cost()
                                   |
                      AWS • Azure • Google Cloud

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🚀 Features

• MCP Server implementation using FastMCP
• MCP Client implementation
• Streamable HTTP transport
• JSON-RPC communication
• Automatic Tool Discovery
• Enterprise Tool Registration
• Error Handling
• Cloud Reference Architectures
• Extensible Tool Framework

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🛠 Technologies

• Python 3.10+
• Model Context Protocol (MCP)
• FastMCP
• Streamable HTTP
• JSON-RPC
• AWS
• Microsoft Azure
• Google Cloud Platform

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📂 Project Structure

enterprise-mcp-python-server/
│
├── client.py
├── server.py
├── requirements.txt
├── README.md
└── .gitignore

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⚙️ Installation

Clone the repository

git clone https://github.com/alann20/enterprise-mcp-python-server.git

cd enterprise-mcp-python-server

Create Virtual Environment

python -m venv .venv

Activate Environment

Windows

.venv\Scripts\Activate.ps1

Linux / macOS

source .venv/bin/activate

Install dependencies

pip install "mcp[cli]>=1.27,<2"

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▶️ Running the Server

python server.py

The MCP endpoint will be available at

http://localhost:8000/mcp

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▶️ Running the Client

Open another terminal

python client.py

Expected Output

Connection initialized successfully

Available Tools

server_status
add_numbers
cloud_architecture

Status OK

25 + 17 = 42

AWS Architecture Loaded

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🔧 Available Tools

server_status()

Returns the current server status.

Example

{
    "status":"available",
    "service":"Enterprise Architecture MCP Server"
}

---

add_numbers()

Adds two numeric values.

Input

{
    "a":25,
    "b":17
}

Output

{
    "result":42
}

---

cloud_architecture()

Returns a reference architecture for a cloud provider.

Supported providers

• AWS
• Azure
• Google Cloud

Example

{
    "provider":"AWS",
    "architecture":{
        "compute":"Amazon EKS",
        "storage":"Amazon S3",
        "database":"Amazon Aurora"
    }
}

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calculate_cloud_cost()

Returns an estimated monthly cloud cost.

Example

{
    "instances":10,
    "monthly_cost_per_instance":1500,
    "estimated_monthly_cost":15000
}

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🧪 Validation

The project validates

• Server startup
• Client initialization
• Tool discovery
• Tool execution
• Structured JSON responses
• Error handling
• Unsupported providers

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💡 Learning Objectives

Understand

• Model Context Protocol
• MCP Client
• MCP Server
• JSON-RPC
• Streamable HTTP
• Tool Discovery
• Enterprise AI Integration

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🔮 Future Improvements

• Authentication (OAuth)
• JWT Authorization
• AWS Integration
• Azure Integration
• Google Cloud Integration
• PostgreSQL
• MongoDB
• Redis
• OpenAI
• Claude
• Gemini
• Vector Databases
• RAG
• LLMOps
• Monitoring
• Docker
• Kubernetes
• CI/CD

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👨‍💻 Author

Michel Alan López Lara

Enterprise Data and AI Architect

• Enterprise Architecture
• Cloud Strategy
• Data Architecture
• AI Governance
• Generative AI

LinkedIn

https://www.linkedin.com/in/michel-alan-l%C3%B3pez-lara-49a88239/

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⭐ If you found this project useful

Give it a ⭐ on GitHub!

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📚 References

• Model Context Protocol Specification
• MCP Python SDK
• JSON-RPC 2.0 Specification
• Python Documentation
• AWS Architecture Center
• Azure Architecture Center
• Google Cloud Architecture Framework

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License

MIT License