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Spring Ai Mcp Server Patterns

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giuseppe-trisciuoglio
spring-ai-mcp-server-patterns

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.

Overview

Publishergiuseppe-trisciuoglio
Repositorydeveloper-kit
Skill namespring-ai-mcp-server-patterns
Stars
345
Forks
41
Bundled files
7
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by giuseppe-trisciuoglio on GitHub. Read the source before you install it.

Installation

Install the Spring Ai Mcp Server Patterns AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/giuseppe-trisciuoglio/developer-kit.git /tmp/developer-kit
mkdir -p .claude/skills
cp -r /tmp/developer-kit/plugins/developer-kit-java/skills/spring-ai-mcp-server-patterns .claude/skills/spring-ai-mcp-server-patterns
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Spring Ai Mcp Server Patterns in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Spring Ai Mcp Server Patterns on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Spring Ai Mcp Server Patterns is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Spring AI MCP Server Implementation Patterns

Implements MCP servers with Spring AI for AI function calling, tool handlers, and MCP transport configuration.

Overview

Production-ready MCP server patterns: @Tool functions, @PromptTemplate resources, and stdio/HTTP/SSE transports with Spring AI security.

When to Use

MCP servers, Spring AI function calling, AI tools, tool calling, custom tool handlers, Spring Boot MCP, resource endpoints, or MCP transport configuration.

Quick Reference

Core Annotations

AnnotationTargetPurpose
@EnableMcpServerClassEnable MCP server auto-configuration
@Tool(description)MethodDeclare AI-callable tool
@ToolParam(value)ParameterDocument tool parameter for AI
@PromptTemplate(name)MethodDeclare reusable prompt template
@PromptParam(value)ParameterDocument prompt parameter

Transport Types

TransportUse CaseConfig
stdioLocal process / Claude DesktopDefault
httpRemote HTTP clientsport, path
sseReal-time streaming clientsport, path

Key Dependencies

xml
<!-- 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
// Gradle
implementation 'org.springframework.ai:spring-ai-mcp-server:1.0.0'
implementation 'org.springframework.ai:spring-ai-starter-model-openai:1.0.0'

Instructions

1. Project Setup

Add Spring AI MCP dependencies (see Quick Reference above), configure the AI model in application.properties, and enable MCP with @EnableMcpServer:

java
@SpringBootApplication
@EnableMcpServer
public class MyMcpApplication {
    public static void main(String[] args) {
        SpringApplication.run(MyMcpApplication.class, args);
    }
}
properties
spring.ai.openai.api-key=${OPENAI_API_KEY}
spring.ai.mcp.enabled=true
spring.ai.mcp.transport.type=stdio

2. Define Tools

Annotate methods with @Tool inside @Component beans. Use @ToolParam to document parameters:

java
@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 for database tools, API integration tools, and the FunctionCallback low-level pattern.

3. Create Prompt Templates

java
@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 for additional prompt template patterns.

4. Configure Transport

yaml
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

5. Add Security

java
@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 for full security patterns.

6. Testing

java
@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/testing-guide.md for integration tests, Testcontainers, security tests, and slice tests.

Best Practices

Tool Design

  • Keep tools focused — one operation per tool
  • Use clear, action-oriented names (getWeather, executeQuery)
  • Always annotate parameters with @ToolParam and descriptive text
  • Return structured records/DTOs, not raw strings or maps
  • Design tools to be idempotent when possible

Security

  • Validate and sanitize all inputs — AI-generated parameters are untrusted
  • Use parameterized queries for SQL; validate and normalize paths for file tools
  • Apply @PreAuthorize for role-based access on sensitive tools
  • Audit log all data-modifying tool executions
  • Never expose credentials or sensitive data in tool descriptions or error messages

Performance

  • Use @Cacheable for expensive operations with appropriate TTL
  • Set timeouts for all external calls
  • Use @Async for long-running operations
  • Monitor with Micrometer metrics

Error Handling

  • Return structured error responses with user-friendly messages
  • Log context (user, tool name, parameters) for debugging
  • Implement retry logic for transient failures
  • Implement @ControllerAdvice for consistent error responses

Examples

Example 1: Minimal Weather MCP Server

java
@SpringBootApplication
@EnableMcpServer
public class WeatherMcpApplication {
    public static void main(String[] args) {
        SpringApplication.run(WeatherMcpApplication.class, args);
    }
}

@Component
public class WeatherTools {

    @Tool(description = "Get current weather for a city")
    public WeatherData getWeather(@ToolParam("City name") String city) {
        return new WeatherData(city, "Sunny", 22.5);
    }
}

record WeatherData(String city, String condition, double temperatureCelsius) {}

Example 2: Secure Database Tool

java
@Component
@PreAuthorize("hasRole('USER')")
public class DatabaseTools {

    private final JdbcTemplate jdbcTemplate;

    @Tool(description = "Execute a read-only SQL query and return results")
    public QueryResult executeQuery(
            @ToolParam("SQL SELECT query") String sql,
            @ToolParam(value = "Parameters as JSON map", required = false) String paramsJson) {

        if (!sql.trim().toUpperCase().startsWith("SELECT")) {
            throw new IllegalArgumentException("Only SELECT queries are allowed");
        }
        List<Map<String, Object>> rows = jdbcTemplate.queryForList(sql);
        return new QueryResult(rows, rows.size());
    }
}

See references/examples.md for complete examples including file system tools, REST API integration, and prompt template servers.

Constraints and Warnings

Security

  • Never expose sensitive data in tool descriptions, parameters, or error messages
  • Input validation is mandatory — always validate before executing
  • External content is untrusted — tools fetching URLs may receive prompt injection payloads; validate all fetched content
  • SQL injection: use parameterized queries exclusively
  • Path traversal: normalize and validate all file paths against a base path

Operational

  • Responses should be concise — large responses can exceed AI context window limits
  • All tools must implement timeouts; default should be configurable
  • Rate limit expensive operations
  • Tools may be called concurrently — ensure thread safety

Spring AI Specific

  • Spring AI is actively developed — pin specific versions in production
  • Error messages thrown by tools are exposed to AI models; sanitize them
  • Choose transport type carefully: stdio for local processes, http/sse for remote clients

References

Consult these files for detailed patterns and examples:

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Spring Ai Mcp Server Patterns AI skill do?

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.

Why use Spring Ai Mcp Server Patterns on TypingMind?

Because you install it once and use it with any model. Spring Ai Mcp Server Patterns is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Spring Ai Mcp Server Patterns in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/giuseppe-trisciuoglio/developer-kit/tree/main/plugins/developer-kit-java/skills/spring-ai-mcp-server-patterns. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Spring Ai Mcp Server Patterns?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Spring Ai Mcp Server Patterns?

As many as you like. As long as a model supports skills, you can use Spring Ai Mcp Server Patterns with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Spring Ai Mcp Server Patterns AI skill free?

Yes. It is published on GitHub by giuseppe-trisciuoglio under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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