springboot-ai-mcp-example

Spring AI MCP Examples

This project demonstrates how to build and integrate a Model-Controller-Prompt (MCP) architecture using Spring Boot and Spring AI. It includes both an MCP Client and an MCP Server, showcasing how AI models can dynamically interact with external tools and data sources (MongoDB, PostgreSQL) to fulfill user requests in real time.

• Full blog: https://blog.nashtechglobal.com/building-an-mcp-server-and-client-using-java-spring-boot-ai/

Features

• Data Source Integration: Connects to a local MongoDB, PostgreSQL to perform various operations.

• Callable Tools with @Tool Annotation: Tool methods are declared using the @Tool annotation, making them discoverable and callable by the AI model at runtime.

• Tool Registration with ToolCallbackProvider: Used to expose and register tools dynamically during initialization, enabling seamless integration with Spring AI.

How It Works

The MCP workflow in these examples follows these steps:

Initial Setup and Discovery

1. Connection Initialization

• When the host application starts, its Chat Client sends an initialization request to the MCP Client.
• The MCP Client establishes a connection to the configured MCP Server.

2. Capability Discovery

• The MCP Client asks the MCP Server: “What capabilities (tools) do you offer?”
• The MCP Server responds with metadata describing all available tools, such as names, parameters, and descriptions.

4. Registration

• The MCP Client forwards this tool list to the Chat Client.
• The Chat Client registers these tools with the AI model (LLM) so it knows what tools are available at runtime.
• The AI model confirms: “These tools are now available for me to use.”

Processing a User Request

Let’s say a user asks: "Get all products"

1. Need Recognition

• The Chat Client receives the user’s message and forwards it to the AI model.
• The AI model analyzes the prompt and determines it requires external data (e.g., a product list from a database).

2. Tool Selection

• The AI model selects a registered tool that can handle the request, e.g., getProductsTool.

3. Tool Invocation

• The Chat Client requests the MCP Client to execute the selected tool.
• The MCP Client sends a standardized request to the MCP Server.
• The MCP Server executes the corresponding tool logic.

4. Data Retrieval

• The tool queries the necessary data source (e.g., MongoDB or PostgreSQL).
• The database responds with the requested product data.
• The tool sends the data back up to the MCP Server, which relays it to the MCP Client.

5. Response Generation

• The MCP Client returns the external data to the AI model.
• The AI model generates a final natural language response that incorporates the data.

6. Result Delivery

• The Chat Client sends the completed response back to the user interface.
• The user sees the answer: e.g., “Here are 10 products currently available.”

Running the Examples

Prerequisites

• Java 21
• SpringBoot 3.5.3
• Spring AI 1.0.0
• MongoDB
• Postgresql
• Docker
• OpenAI API key

Step 1: Run Docker Compose to set up MongoDB and PostgreSQL with init data

docker compose up -d

Step 2: Update application.yaml with OpenAI API Key:

• Open API Key
• Get OpenAI API key [here] (https://platform.openai.com/api-keys)

Step 3: Run MCP Server

cd mcp-server
./mvnw spring-boot:run

Step 4: Run MCP Client

cd mcp-client
./mvnw spring-boot:run

Step 5: Test in Postman

• Test with PostgreSQL

curl --location 'localhost:8081/chat/ask' \
--header 'Content-Type: text/plain' \
--data 'Requirement: [Postgres] In the table orders, retrieve all orders that have status COMPLETED

Rules:
1. Respond strictly in JSON format, conforming to RFC 8259.
2. The JSON structure must align with the java.util.HashMap format.
3. Do not include explanations, comments, or any labels like ```json```.'

• Test with MongoDB

curl --location 'localhost:8081/chat/ask' \
--header 'Content-Type: text/plain' \
--data 'Requirement: Retrieve all documents from the product-service collection, products table, where the price > 100

Rules:
1. Respond strictly in JSON format, conforming to RFC 8259.
2. The JSON structure must align with the java.util.HashMap format.
3. Do not include explanations, comments, or any labels like ```json```.'

PostgreSQL	MongoDB

References

• https://docs.spring.io/spring-ai/reference/api/mcp/mcp-overview.html
• Supported by ChatGPT