How to Use Spring AI MCP with Chat2DB for Natural Language Database Queries

Introduction

In the previous article we introduced the basics of Spring AI MCP (Model Context Protocol) and demonstrated file operations. This article dives deeper, showing how to use MCP for natural‑language interaction with databases.

What is Chat2DB?

Chat2DB is an innovative database interaction tool that lets users query and manipulate databases using natural language. By leveraging large language models (LLMs), it translates everyday questions into appropriate SQL statements.

MCP (Model Context Protocol) Overview

MCP is a protocol designed to give LLMs read‑only access to databases. Its main features include:

Read‑only database access interface

Allows LLMs to inspect database schema

Supports execution of read‑only queries

Ensures data security

In a PostgreSQL scenario, the MCP server acts as a bridge between the database and the LLM, enabling safe understanding and querying of the database structure.

Spring AI Integration

Dependency Configuration

<code>&lt;dependency&gt;
    &lt;groupId&gt;org.springframework.ai&lt;/groupId&gt;
    &lt;artifactId&gt;spring-ai-openai-spring-boot-starter&lt;/artifactId&gt;
&lt;/dependency&gt;

&lt;dependency&gt;
    &lt;groupId&gt;org.springframework.experimental&lt;/groupId&gt;
    &lt;artifactId&gt;spring-ai-mcp&lt;/artifactId&gt;
    &lt;version&gt;0.4.1&lt;/version&gt;
&lt;/dependency&gt;</code>

Model Configuration

<code>spring.ai.openai.chat.options.model=deepseek-chat
spring.ai.openai.base-url=https://api.deepseek.com
spring.ai.openai.api-key=sk-XXX</code>

Core Implementation

The following Java code implements the MCP client and chat functionality:

<code>@Bean
public CommandLineRunner predefinedQuestions(ChatClient.Builder chatClientBuilder,
        List&lt;McpFunctionCallback&gt; functionCallbacks, ConfigurableApplicationContext context) {
    return args -> {
        var chatClient = chatClientBuilder
                .defaultFunctions(functionCallbacks.toArray(new McpFunctionCallback[0]))
                .build();
        String question1 = "你能链接我的postgres数据库，然后告诉我这个数据库有多少张表吗？";
        System.out.println("ASSISTANT: " + chatClient.prompt(question1).call().content());

        String question2 = "你能链接我的postgres数据库，然后告诉我这个商品表里面哪个车最贵吗？";
        System.out.println("ASSISTANT: " + chatClient.prompt(question2).call().content());
        context.close();
    };
}

@Bean
public List&lt;McpFunctionCallback&gt; functionCallbacks(McpSyncClient mcpClient) {
    return mcpClient.listTools(null)
            .tools()
            .stream()
            .map(tool -> new McpFunctionCallback(mcpClient, tool))
            .toList();
}

@Bean(destroyMethod = "close")
public McpSyncClient mcpClient() {
    var stdioParams = ServerParameters.builder("npx")
            .args("-y", "@modelcontextprotocol/server-postgres",
                  "postgresql://postgres:lengleng@localhost:5432")
            .build();
    var mcpClient = McpClient.using(new StdioClientTransport(stdioParams))
            .requestTimeout(Duration.ofSeconds(10)).sync();
    var init = mcpClient.initialize();
    System.out.println("MCP Initialized: " + init);
    return mcpClient;
}</code>

Database Schema Example

Execution Flow Diagram

Detailed Execution Steps

Initialization Phase

Spring application starts

MCP client initialized with protocol version 2024-11-05

Server info: example-servers/postgres v0.1.0

Tool Registration Phase

Register query tool for read‑only SQL queries

Set query timeout to 10 seconds

Database Interaction Phase

Perform connection check

Retrieve database table structure

Execute user query and return results

Response Handling Phase

Convert query results to structured JSON

LLM processes result and generates natural‑language answer

Return formatted response to user

Sample Queries

<code>-- Query table count
SELECT COUNT(*) as count FROM information_schema.tables WHERE table_schema = 'public';

-- Query table schema
SELECT column_name, data_type FROM information_schema.columns WHERE table_name = 'products';

-- Query product data
SELECT id, name, price FROM products ORDER BY price DESC;</code>

Conclusion

Chat2DB combined with Spring AI MCP provides a revolutionary solution for database interaction. By using natural‑language processing and a secure protocol design, it offers ease of use while guaranteeing data safety, making it ideal for scenarios that require frequent queries with a low technical barrier.