chunking-strategy
Provides chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages (10-20%), and semantic boundary…
Provides Spring Boot MCP server patterns that create Model Context Protocol servers with Spring AI by defining tool handlers, exposing resources, configuring prompt templates, and setting up transports for AI function calling and tool calling. Use when building MCP servers to
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Provides Spring Boot MCP server patterns that create Model Context Protocol servers with Spring AI by defining tool handlers, exposing resources, configuring prompt templates, and setting up transports for AI function calling and tool calling. Use when building MCP servers to
name: spring-ai-mcp-server-patterns description: Provides Spring Boot MCP server patterns that create Model Context Protocol servers with Spring AI by defining tool handlers, exposing resources, configuring prompt templates, and setting up transports for AI function calling and tool calling. Use when building MCP servers to extend AI capabilities with Spring's official AI framework, implementing AI tools, custom function calling, or MCP client integration. allowed-tools: Read, Write, Edit, Bash, Glob, Grep
Implements MCP servers with Spring AI for AI function calling, tool handlers, and MCP transport configuration.
Production-ready MCP server patterns: `@Tool` functions, `@PromptTemplate` resources, and stdio/HTTP/SSE transports with Spring AI security.
MCP servers, Spring AI function calling, AI tools, tool calling, custom tool handlers, Spring Boot MCP, resource endpoints, or MCP transport configuration.
| Annotation | Target | Purpose | |-----------|--------|---------| | `@EnableMcpServer` | Class | Enable MCP server auto-configuration | | `@Tool(description)` | Method | Declare AI-callable tool | | `@ToolParam(value)` | Parameter | Document tool parameter for AI | | `@PromptTemplate(name)` | Method | Declare reusable prompt template | | `@PromptParam(value)` | Parameter | Document prompt parameter |
| Transport | Use Case | Config | |-----------|----------|--------| | `stdio` | Local process / Claude Desktop | Default | | `http` | Remote HTTP clients | `port`, `path` | | `sse` | Real-time streaming clients | `port`, `path` |
<!-- Maven -->
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-mcp-server</artifactId>
<version>1.0.0</version>
</dependency>
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-starter-model-openai</artifactId>
<version>1.0.0</version>
</dependency>// Gradle implementation 'org.springframework.ai:spring-ai-mcp-server:1.0.0' implementation 'org.springframework.ai:spring-ai-starter-model-openai:1.0.0'
Add Spring AI MCP dependencies (see Quick Reference above), configure the AI model in `application.properties`, and enable MCP with `@EnableMcpServer`:
@SpringBootApplication
@EnableMcpServer
public class MyMcpApplication {
public static void main(String[] args) {
SpringApplication.run(MyMcpApplication.class, args);
}
}spring.ai.openai.api-key=${OPENAI_API_KEY}
spring.ai.mcp.enabled=true
spring.ai.mcp.transport.type=stdioAnnotate methods with `@Tool` inside `@Component` beans. Use `@ToolParam` to document parameters:
@Component
public class WeatherTools {
@Tool(description = "Get current weather for a city")
public WeatherData getWeather(@ToolParam("City name") String city) {
return weatherService.getCurrentWeather(city);
}
@Tool(description = "Get 5-day forecast for a city")
public ForecastData getForecast(
@ToolParam("City name") String city,
@ToolParam(value = "Unit: celsius or fahrenheit", required = false) String unit) {
return weatherService.getForecast(city, unit != null ? unit : "celsius");
}
}See [references/implementation-patterns.md](references/implementation-patterns.md) for database tools, API integration tools, and the `FunctionCallback` low-level pattern.
@Component
public class CodeReviewPrompts {
@PromptTemplate(
name = "java-code-review",
description = "Review Java code for best practices and issues"
)
public Prompt createCodeReviewPrompt(
@PromptParam("code") String code,
@PromptParam(value = "focusAreas", required = false) List<String> focusAreas) {
String focus = focusAreas != null ? String.join(", ", focusAreas) : "general best practices";
return Prompt.builder()
.system("You are an expert Java code reviewer with 20 years of experience.")
.user("Review the following Java code for " + focus + ":\n```java\n" + code + "\n```")
.build();
}
}See [references/implementation-patterns.md](references/implementation-patterns.md) for additional prompt template patterns.
spring:
ai:
mcp:
enabled: true
transport:
type: stdio # stdio | http | sse
http:
port: 8080
path: /mcp
server:
name: my-mcp-server
version: 1.0.0@Configuration
public class McpSecurityConfig {
@Bean
public ToolFilter toolFilter(SecurityService securityService) {
return (tool, context) -> {
User user = securityService.getCurrentUser();
if (tool.name().startsWith("admin_")) {
return user.hasRole("ADMIN");
}
return securityService.isToolAllowed(user, tool.name());
};
}
}Use `@PreAuthorize("hasRole('ADMIN')")` on tool methods for method-level security. See [references/implementation-patterns.md](references/implementation-patterns.md) for full security patterns.
@SpringBootTest
class WeatherToolsTest {
@Autowired
private WeatherTools weatherTools;
@MockBean
private WeatherService weatherService;
@Test
void testGetWeather_Success() {
when(weatherService.getCurrentWeather("London"))
.thenReturn(new WeatherData("London", "Cloudy", 15.0));
WeatherData result = weatherTools.getWeather("London");
assertThat(result.city()).isEqualTo("London");
verify(weatherService).getCurrentWeather("London");
}
}See [references/testi
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Repo: giuseppe-trisciuoglio/developer-kit
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