anthropic-mcp-ai

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🧠 Name Analyzer with AI

This project is a Proof of Concept (POC) for implementing the Model Context Protocol (MCP) to track the full lifecycle of an API request, enrich it with structured data, and use AI (GPT-4o / GPT-3.5) to generate an automatic technical summary.


🚀 Features
  • 🧾 Request lifecycle trace using the MCPContext abstraction
  • 🌐 External API integrations:
  • 🧠 AI-powered analysis using OpenAI's GPT models
  • 📄 OpenAPI 3.0 documentation served via Swagger UI
  • 📦 Clean architecture using Spring Boot 3 + Java 17

📐 Architecture
Client
  |
  v
/analyze [POST] <----> AnalyzeController
                         |
                         v
                  AnalyzerService
                         |
            +------------+------------+
            |                         |
        AgifyClient             GenderizeClient
            |                         |
            +------------+------------+
                         |
                      MCPContext  ← records full trace
                         |
                         v
                     IAService  → OpenAI call
                         |
                         v
                  AnalyzeResponse (enriched)

🔧 Tech Stack
  • Java 17 ☕️
  • Spring Boot 3 ⚙️
  • Spring WebFlux (WebClient) 🌐
  • Lombok 🧬
  • OpenAI API (Chat Completions – GPT-4o / GPT-3.5) 🤖

📥 How to Run
# Clone the repository
git clone https://github.com/your-username/anthropic-mcp-ai.git
cd anthropic-mcp-ai

# Make sure you have Java 17 installed
./mvnw spring-boot:run

🔐 Environment Configuration

Set these variables in your environment or in a .env file:

OPENAI_API_KEY=sk-XXXXXXXXXXXXXXXXXXXXXXXXXXX

These values will be loaded via the AIProperties class using @ConfigurationProperties.


🧪 Try It

Once the server is running, access:

{
  "name": "gabriel"
}

You will receive a structured response with the AI-generated explanation in the technicalExplanation field.


Enjoy! 😉
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