Production-Grade MCP Agents: Proof of Concept

"From Chaos to Contract: Building Reliable AI Systems with the Model Context Protocol"

This repository contains the reference implementation for the article "Building Production-Grade AI Agents with MCP & A2A". It demonstrates a robust, standardized architecture for Agent-to-Agent (A2A) communication using the Model Context Protocol (MCP).

🎯 Project Overview

This Proof of Concept (PoC) implements a "Daily Minutes Assistant"—a production-patterned system where an orchestration agent intelligently coordinates with specialized sub-services (Search, Calendar, etc.) via strict protocols rather than fragile prompts.

The core philosophy demonstrated here is: Context Engineering > Prompt Engineering.

🏗️ Architecture Design

The system relies on a hub-and-spoke model where the standard MCP protocol facilitates dynamic tool discovery and resource access.

graph TD
User([User]) -->|Query| Orchestrator[Orchestrator Agent]

subgraph "MCP Ecosystem"
Orchestrator <-->|MCP Protocol| Search[Search Server]
Orchestrator <-->|MCP Protocol| Calendar[Calendar Resource]
Orchestrator <-->|MCP Protocol| Config[Config Service]
end

Search -->|returns| Context[Search Results]
Calendar -->|returns| Events[Meeting Data]

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✨ Key Features

• FastMCP Implementation: Rapid server development using mcp.server.fastmcp.
• Typed Tool Contracts: Automatic JSON-schema generation from Python type hints.
• Resource Exposure: Static and dynamic context serving via config:// URIs.
• Stdio Transport: Zero-latency, secure local communication channels.
• Production Directory Structure: Clean separation of server, client, and testing logic.

🚀 Quick Start

Prerequisites

• Python 3.10+
• pip (Python Package Installer)

Installation

Clone the Repository

git clone https://github.com/aniket-work/production-grade-mcp-agents.git
cd production-grade-mcp-agents

Install Dependencies

pip install -r requirements.txt

Running the Agent

This PoC includes a self-contained client that handles the server lifecycle automatically.

python src/client/agent_client.py

Expected Output:

Connected to server. Found 2 tools.
Executing search_web tool...
Result: Mock search results for 'MCP adoption trends'...
Reading resource config://app_settings...

📂 Project Structure

├── src/
│   ├── server/
│   │   └── agent_server.py    # The 'Tools' & 'Resources' Provider
│   └── client/
│       └── agent_client.py    # The 'Brain' / Consumer
├── title_card.png             # Project Asset
├── architecture.png           # Architecture Design
├── requirements.txt           # Dependency Spec
└── README.md                  # Documentation

🤝 Contributing

This is an experimental educational project. Feel free to fork and experiment with connecting real APIs (Tavily, Google Calendar, etc.) to the defined MCP endpoints.

📜 License

MIT License. See LICENSE for details.

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Created by Aniket