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Arvind Menon
Arvind Menon

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How I Connected Claude to Maven Central (And Why You Should Too)

#mcp #springboot #java #ai

A Spring developer's journey into Model Context Protocol

So here's the thing - I've been working with Spring and the JVM ecosystem for years, mostly in enterprise environments. Recently, I've gotten really fascinated with LLMs and started wondering: could I combine my Spring knowledge with AI to solve some of the annoying daily problems we all face?

One problem that's always bugged me is getting quick dependency information. You know the drill - checking for updates across Maven and Gradle projects, trying to figure out which versions are safe to upgrade, spending way too much time on Maven Central looking up version histories. It's tedious work when you just need quick answers.

Then I discovered the Model Context Protocol (MCP) - this new standard that lets you extend AI assistants with custom tools. That got me thinking: what if I could give Claude or GitHub Copilot direct access to Maven Central data? What if instead of manually checking dependencies, I could just ask my AI assistant?

That curiosity led me to build the Maven Tools MCP Server. It's not trying to replace sophisticated tools like Dependabot or enterprise CI/CD solutions that handle security compliance and automated updates. It's simply designed for when you need quick dependency information through your AI assistant.

What Actually Happened When I Started Using It

Before I dive into the technical stuff, let me show you what this looks like in practice. Instead of opening 15 browser tabs to check my Maven dependencies, I can now just paste my build file and ask:

"Check all these dependencies for updates, but only show me stable versions since this is going to production"

The AI assistant instantly analyses everything and tells me which ones need attention, what kind of updates they are (major/minor/patch), and even warns me about potential breaking changes. It's like having a dependency expert sitting next to you.

The Technical Challenge (And Why Spring Made It Easy)

Building an MCP server sounds intimidating, but Spring Boot made it surprisingly straightforward. The core challenge was creating a bridge between AI assistants and Maven Central's API that could:

  • Handle Maven and Gradle projects (both use Maven coordinates anyway)
  • Process bulk requests efficiently
  • Classify versions intelligently (stable vs RC vs beta)
  • Cache aggressively since dependency data doesn't change that often

Here's what the foundation looks like:

<dependencies>
    <!-- This is the magic - Spring AI's MCP support -->
    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-starter-mcp-server</artifactId>
    </dependency>

    <!-- Good old Caffeine for caching -->
    <dependency>
        <groupId>com.github.ben-manes.caffeine</groupId>
        <artifactId>caffeine</artifactId>
    </dependency>

    <!-- Maven's own version comparison logic -->
    <dependency>
        <groupId>org.apache.maven</groupId>
        <artifactId>maven-artifact</artifactId>
    </dependency>
</dependencies>
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The beautiful thing about Spring AI's MCP support is that you just annotate your methods and it handles all the protocol stuff:

  @Service
  public class MavenDependencyTools {

    @Tool(description = "Get latest version of any dependency from Maven Central with version type analysis")
    public ToolResponse get_latest_version(
        @ToolParam(description = "Maven coordinate like 'org.springframework:spring-core'")
        String dependency,
        @ToolParam(description = "Prioritize stable versions", required = false)
        boolean preferStable) {

      return executeToolOperation(() -> {
        MavenCoordinate coordinate = MavenCoordinateParser.parse(dependency);
        List<String> allVersions = mavenCentralService.getAllVersions(coordinate);

        if (allVersions.isEmpty()) {
          return notFoundResponse(coordinate);
        }

        return buildVersionsByType(coordinate, allVersions, preferStable);
      });
    }
  }
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Spring AI's spring-ai-starter-mcp-server handles the JSON serialization, parameter validation, stdio transport, and all that protocol plumbing. I just focus on the business logic. This was honestly the game-changer - without Spring AI's MCP starter, I would have had to implement the entire MCP protocol from scratch.

What Makes This Different From Just Googling

I know what you're thinking - "can't I just search for this stuff?" Sure, but here's why this approach is actually much better:

Speed: A single bulk request analyzes 20+ dependencies in under 500ms. Try doing that manually.

Accuracy: Direct API access to Maven Central means you're always getting current data, not some random blog post from 2019.

Intelligence: The server doesn't just return version numbers. It classifies them (stable/RC/beta), compares your current versions, and can even predict maintenance patterns.

Context: Your AI assistant can combine dependency information with other knowledge. Ask "should I upgrade Spring Boot from 2.7 to 3.0 and what will break?" and it actually knows what might break.

Here's a real example of what the server returns:

{
  "dependency": "org.springframework:spring-core",
  "latest_stable": { "version": "6.2.7", "type": "stable" },
  "latest_rc": { "version": "7.0.0-RC1", "type": "rc" },
  "latest_beta": { "version": "7.0.0-beta1", "type": "beta" },
  "update_summary": {
    "major_updates": 1,
    "minor_updates": 0, 
    "patch_updates": 2
  }
}
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Advanced Dependency Intelligence

Once I had the basic version lookup working, I realized there was an opportunity to provide more sophisticated dependency analysis. The latest version includes:

Dependency Age Analysis: Is your Spring Boot version "fresh" (< 30 days), "current" (< 90 days), "aging" (< 180 days), or "stale"?

Release Pattern Prediction: Based on historical data, when might the next Jackson release happen?

Project Health Scoring: Give me a health score for all my dependencies and tell me which ones need attention.

This stuff is genuinely useful. I've caught several dependencies that hadn't been updated in over a year and were becoming maintenance risks.

Getting Started (It's Easier Than You Think)

The whole thing runs in Docker, so setup is pretty straightforward. For Claude Desktop, you just add this to your config:

{
  "mcpServers": {
    "maven-tools": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm", "-e", "SPRING_PROFILES_ACTIVE=docker",
        "arvindand/maven-tools-mcp:latest"
      ]
    }
  }
}
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For VS Code with GitHub Copilot, it's similar - just point it at the Docker container and you're good to go.

Then you can start asking questions like:

  • "What's the latest stable Spring Boot version?"
  • "Check my build.gradle for outdated dependencies"
  • "Show me the health status of my project dependencies"
  • "Should I upgrade from Hibernate 5.6 to 6.x?"

Performance Notes (Because That Matters)

I spent some time optimizing this thing because nobody wants to wait 10 seconds for dependency info:

  • Caching: Caffeine cache with 24-hour TTL hits about 90% of repeated requests
  • Bulk Operations: Single API calls can handle 20+ dependencies
  • Native Images: Docker images use GraalVM native compilation for fast startup
  • Smart Filtering: Client-side filtering reduces unnecessary API calls

The result? Most queries return in under 100ms once the cache is warm.

What I Learned Building This

  1. MCP is actually pretty cool: The protocol makes it surprisingly easy to extend AI assistants. Spring AI's support makes it even easier.

  2. Caching is important: Dependency data doesn't change that often, so aggressive caching makes the user experience much better.

  3. Bulk operations matter: Once you can analyze 20+ dependencies at once, it changes how you think about dependency management.

  4. Spring Boot works great for this: The entire MCP server, caching, HTTP client, configuration - Spring handles it all elegantly.

  5. Native images are worth it: GraalVM compilation means the Docker container starts instantly and uses minimal memory.

Help Me Make This Better

I'd really love to get more feedback and see if other developers find this useful for their workflow.

If you try it out, please:

  • ⭐ Star the repo at github.com/arvindand/maven-tools-mcp
  • Let me know what works and what doesn't
  • Open issues for features you'd like to see
  • Share your experience if you find it useful

I'm particularly curious to hear from teams using different build tools or anyone working on similar AI integration projects.

Source Code and Documentation

Everything's open source with MIT license:

The README has detailed examples and troubleshooting info. If you run into issues, open a GitHub issue and I'll help you get it working.

This started as a weekend experiment combining my Spring background with curiosity about LLMs and developer productivity. It ended up being something I actually use daily and genuinely miss when I'm working on machines that don't have it set up.

The intersection of AI and developer tooling feels like it's just getting started. If you're interested in MCP development, Spring AI, or just want to see what's possible when you give AI assistants access to real data, give this a try.

What other development workflows do you think could benefit from AI integration? Have you experimented with extending AI assistants? I'd love to hear about your experiences in the comments.

About the Author: I'm Arvind, a backend developer based in Karlsruhe, Germany. I spend most of my time with Java and Spring Boot in enterprise environments, but lately I've been exploring AI integration and building up my open source presence. You can find me on GitHub or LinkedIn.

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