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README
๐ง 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.
๐ Features
- ๐งพ Request lifecycle trace using the
MCPContextabstraction - ๐ External API integrations:
agify.ioโ estimate agegenderize.ioโ estimate gender
- ๐ง AI-powered analysis using OpenAI's GPT models
- ๐ OpenAPI 3.0 documentation served via Swagger UI
- ๐ฆ Clean architecture using Spring Boot 3 + Java 17
๐ Architecture
Client
|
v
/analyze [POST] <----> AnalyzeController
|
v
AnalyzerService
|
+------------+------------+
| |
AgifyClient GenderizeClient
| |
+------------+------------+
|
MCPContext โ records full trace
|
v
IAService โ OpenAI call
|
v
AnalyzeResponse (enriched)
๐ง Tech Stack
- Java 17 โ๏ธ
- Spring Boot 3 โ๏ธ
- Spring WebFlux (WebClient) ๐
- Lombok ๐งฌ
- OpenAI API (Chat Completions โ GPT-4o / GPT-3.5) ๐ค
๐ฅ 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
๐ 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.
๐งช Try It
Once the server is running, access:
- Swagger UI: http://localhost:8080/swagger-ui.html
- Try a POST to
/analyzewith:
{
"name": "gabriel"
}
You will receive a structured response with the AI-generated explanation in the technicalExplanation field.