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Arvind Menon
Arvind Menon

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

#java #springboot #ai #mcp

A Spring developer's journey into the Model Context Protocol

I've spent almost a decade building and maintaining Spring/JVM systems in enterprise environments. As I've started learning more about AI—and integrating it into my daily workflows—I had a simple idea: use what I already know (Spring) to shave off the small, annoying bits of daily dev work.

Lately I've been trying to use AI for the parts of development that are mostly "mechanical". One of the first places I felt it: dependency lookups in bigger projects.

In a bigger codebase it's rarely one lookup. It's the same loop repeated across a long list of libraries: search the artifact, find the latest stable version, check whether it's still actively maintained, maybe skim for CVEs… and move on to the next one.

None of that is complicated. It's just time.

When I came across the Model Context Protocol (MCP)—a standard for giving AI assistants access to tools—I realized I could turn that loop into something Claude (or GitHub Copilot) could do directly. Same answers, fewer tabs, and I stay in the editor.

That's how Maven Tools MCP Server happened. It's not meant to replace Dependabot, Renovate, or the kind of CI/CD setup you need for compliance and automated updates. This is for the "I just need to know what's going on with my dependencies right now" moments—through the AI assistant you're already using.

What It Looks Like

Before the technical bits, here's what this looks like in real life. Instead of opening a pile of browser tabs, I 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 assistant checks the dependencies, tells me what needs attention, and groups available updates as major, minor, or patch. Those categories help me decide what to investigate; they don't guarantee compatibility.

The Technical Challenge (And Why Spring Made It Easy)

Building an MCP server sounds like one of those "cool idea, lots of plumbing" projects. Spring Boot made it much less painful than I expected. The main job was building a bridge between AI assistants and Maven Central's API that can:

  • Work across JVM build tools because everything ultimately maps to Maven coordinates (Maven/Gradle/SBT/Mill)
  • Process bulk requests efficiently
  • Classify versions intelligently (stable/RC/beta/alpha/milestone)
  • Cache aggressively since dependency data doesn't change that often

Here's what the foundation looks like:

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

    <!-- Optional MCP client integration (e.g., exposing Context7 tools) -->
    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-starter-mcp-client-webflux</artifactId>
    </dependency>

    <!-- Spring cache abstraction -->
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-cache</artifactId>
    </dependency>

    <!-- 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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With Spring AI's MCP support, you expose methods as tools and let the starter handle the protocol:

@Service
public class MavenDependencyTools {

    @Tool(description = "Single dependency. Returns newest versions by type (stable/rc/beta/alpha/milestone).")
    public ToolResponse get_latest_version(
        @ToolParam(description = "Maven coordinate like 'org.springframework:spring-core'")
        String dependency,
        @ToolParam(description = "Stability filter (ALL, STABLE_ONLY, PREFER_STABLE)", required = false)
        @Nullable StabilityFilter stabilityFilter) {

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

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

            StabilityFilter filter = stabilityFilter != null ? stabilityFilter : StabilityFilter.PREFER_STABLE;
            return buildVersionsByType(coordinate, allVersions, filter);
        });
    }
}
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spring-ai-starter-mcp-server handles JSON serialization, parameter validation, stdio transport, and the MCP protocol bits. Without it, this would have been a much longer project.

What Makes This Different From Just Googling

Fair question. You can absolutely Google it or check Maven Central manually. The difference is what you get when you put it behind a tool designed for bulk + context:

Speed: 20+ dependencies in a single interaction, not 20 separate lookups.

Accuracy: Pulling directly from Maven Central, not from a blog post that happened to mention a version number.

Signal, not just numbers: Classifies versions (stable/RC/beta/etc.), compares properly, and uses historical data to infer release patterns.

Context: Ask follow-ups like "should I move from Spring Boot 2.7 to 3.x—what breaks?" and get an answer that uses both dependency data and ecosystem knowledge.

Example response from a bulk upgrade check:

{
  "comparison_date": "2026-01-06T12:00:00Z",
  "dependencies": [
    {
      "dependency": "org.springframework:spring-core",
      "current_version": "6.1.0",
      "latest_version": "6.2.7",
      "latest_type": "stable",
      "update_type": "minor",
      "update_available": true,
      "status": "success"
    }
  ],
  "update_summary": { "major_updates": 0, "minor_updates": 1, "patch_updates": 0, "no_updates": 0 }
}
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Beyond Version Lookup

Once the basic "latest version" flow worked, it was obvious there was more useful stuff to return.

Dependency Age Analysis: Is a library "fresh" (≤30 days), "current" (≤6 months), "aging" (6 months–2 years), or "stale" (>2 years)?

Release patterns: How often has this library released, and how consistent has that timing been? The server also estimates the next release window from those intervals.

Project Health Scoring: Aggregate health score across dependencies. Which ones need attention?

I treat these as signals to investigate. A quiet release history doesn't by itself mean a library is abandoned, and a stable version label doesn't establish that an upgrade is compatible with my application.

This has already caught a few "we haven't looked at this in forever" dependencies—things that weren't on fire today, but were quietly becoming a maintenance risk.

Getting Started

It runs in Docker. For Claude Desktop, add this to your config:

{
  "mcpServers": {
    "maven-tools": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "arvindand/maven-tools-mcp:latest"
      ]
    }
  }
}
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For VS Code + GitHub Copilot, create .vscode/mcp.json:

{
  "servers": {
    "maven-tools": {
      "type": "stdio",
      "command": "docker",
      "args": ["run", "-i", "--rm", "arvindand/maven-tools-mcp:latest"]
    }
  }
}
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Then ask things 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?"

For a Gradle build, the assistant needs to extract the dependency coordinates before calling the tools. The server's whole-file analysis supports Maven POMs.

Performance

I spent time on this because if it's slow, you'll stop using it:

  • Caching: Maven Central results cached with 24-hour TTL (vulnerability lookups cached separately)
  • Bulk Operations: One request handles 20+ dependencies
  • Native Images: GraalVM compilation for fast startup
  • Backpressure: Bounded concurrency so you don't melt your machine or Maven Central

Once cache is warm, most queries feel instant.

What I Learned

MCP let me make dependency checks available inside the assistant, and Spring AI handled much of the integration work.

Caching matters. Dependency data doesn't change often. Aggressive caching makes everything feel instant.

Bulk operations change the workflow. Checking a bunch of dependencies at once makes this kind of work much less tedious.

Spring Boot fits well. MCP server wiring, caching, HTTP client, configuration—Spring handles the boring parts.

I used GraalVM native compilation to keep startup time and memory use low. That matters for a tool launched alongside the assistant.

Source Code

Everything's MIT licensed:

If you try it, let me know what works and what doesn't. Issues and feedback welcome.

This started as a weekend experiment—Spring + curiosity about AI and MCP + impatience with dependency busywork. It turned into something I use daily and notice when it's missing.

About the Author: I'm a software architect at Rockwell Automation. I write about architecture, developer tooling, and the use of AI in software engineering. You can find me on GitHub or LinkedIn.

Updated September 2026 to clarify tool behaviour and limitations.

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