Dependency intelligence infrastructure for AI assistants and agents working with JVM projects. Traditional upgrade workflow: 1. Google "Spring Boot 2.7 to 3.2 migration guide" (5 minutes) 2. Read 50-page migration docs, cross-reference breaking changes (15 minutes) 3. Check Maven Central for compatible dependency versions (5 minutes) 4. Update pom.xml, compile, fix breaking changes (30 minutes) 5.
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npx mdskills install arvindand/maven-tools-mcpComprehensive Maven dependency intelligence with excellent setup docs and real-world examples
Dependency intelligence infrastructure for AI assistants and agents working with JVM projects.
Traditional upgrade workflow:
Total: ~3 hours, high error rate, production risks
AI-powered workflow:
javax.* → jakarta.*, Hibernate 5→6, Security API changesTotal: 30 seconds for complete plan, ~30 minutes for AI-assisted implementation vs 3+ hours manual
Traditional dependency bots (Renovate, Dependabot) can update version numbers but can't fix the code when APIs break.
AI agents with Maven Tools MCP can:
This is infrastructure for the next generation of automated dependency management.
Developer → GitHub Copilot Chat (VS Code): "Check all latest versions of the dependencies in my pom.xml"
Copilot (via MCP): Instantly analyzes all dependencies and provides:
Time: Seconds vs minutes of manual checking Confidence: High (real-time data from Maven Central)
Developer → Aider/Claude Code: "Upgrade my Spring Boot project from 2.7 to 3.2"
AI Agent workflow:
javax.* → jakarta.*, deprecated APIs, config changesTraditional bots stop at step 4. AI agents complete the job.
See also: 🤖 Dogfooding: Weekly Self-Updating Dependencies for the real in-repo GitHub Actions + agent workflow that applies this pattern to Maven Tools MCP itself.
Ask: "I'm building a REST API for IoT data ingestion. I need high-throughput JSON parsing, time-series data structures, and async HTTP client. What libraries should I use with Spring Boot 3.2?"
Get:
Result: Production-ready stack in 30 seconds vs hours of research
Ask: "Should I use Redis or Caffeine for caching ~10k req/min in Spring Boot?"
Get: Comparative analysis:
Result: Architectural decision made with confidence, not guesswork
Ask: "This Renovate PR upgrades Spring Boot 2.7.18 → 3.2.1. What will break?"
Get: Breaking change analysis:
javax.* → jakarta.* namespace migrationResult: Know before merging, not after production breaks
Ask: "Check these dependencies for CVEs and show me safe upgrade paths: org.springframework:spring-core:5.3.20, com.fasterxml.jackson.core:jackson-databind:2.13.3"
Get:
Result: Immediate vulnerability awareness with clear remediation path
Ask: "Analyze my project's dependency health" (paste pom.xml)
Get: Health report:
Result: Complete project health snapshot in 2yr)
CVE Vulnerability Scanning:
License Compliance:
Production-Ready Speed:
{
"mcpServers": {
"maven-tools": {
"command": "docker",
"args": ["run", "-i", "--rm", "arvindand/maven-tools-mcp:latest-noc7"]
}
}
}
Solution (keep Context7 enabled): CONTEXT7_API_KEY is optional, but if your environment requires Context7 auth (or you want to avoid anonymous limits), export it and pass it through Docker with -e CONTEXT7_API_KEY in your MCP client config.
Symptom: SSL handshake failures, certificate errors Cause: Corporate MITM proxy with custom CA certificates Solution: Build custom image with corporate certificates - see CORPORATE-CERTIFICATES.md
Symptom: First query takes 2-3 seconds Cause: Cold start + uncached Maven Central request Expected behavior: Subsequent queries <100ms (cached). This is normal.
Symptom: "permission denied" when running Docker command Solution:
# Linux: Add user to docker group
sudo usermod -aG docker $USER
# Then logout/login
# Windows/Mac: Ensure Docker Desktop is running
1. Zero External State
2. Fail-Fast with Graceful Degradation
3. Production-Ready Performance
AI Assistant → MCP Protocol → Maven Tools MCP Server
Workflow:
1. AI assistant parses user request and extracts dependency coordinates
2. AI assistant calls MCP tool with coordinates (groupId:artifactId)
3. Server fetches maven-metadata.xml from Maven Central
4. Server parses XML, classifies versions by stability
5. Optionally: Server queries OSV.dev for CVEs
6. Optionally: Server fetches license from POM
7. Server returns structured JSON to AI assistant
8. AI assistant interprets results and responds to user
Response time: <100ms (cached), <500ms (fresh)
Docker STDIO (Recommended for Desktop):
docker run -i --rm arvindand/maven-tools-mcp:latestdocker run -i --rm -e CONTEXT7_API_KEY arvindand/maven-tools-mcp:latestDocker HTTP (Streamable HTTP Transport):
/actuator/health/liveness, /actuator/health/readinessdocker run -p 8080:8080 arvindand/maven-tools-mcp:latest-httpdocker run -p 8080:8080 -e CONTEXT7_API_KEY arvindand/maven-tools-mcp:latest-httpNative Image:
JVM JAR:
java -jar maven-tools-mcp.jarOutbound HTTPS to:
repo1.maven.org (Maven Central metadata)api.osv.dev (CVE vulnerability data)mcp.context7.com (optional documentation)Corporate Networks:
arvindand/maven-tools-mcp:latest-noc7)arvindand/maven-tools-mcp:latest-http)Alternative Claude Desktop configuration (if you prefer compose):
Download docker-compose.yml and configure:
{
"mcpServers": {
"maven-tools": {
"command": "docker",
"args": [
"compose", "-f", "/absolute/path/to/docker-compose.yml",
"run", "--rm", "maven-tools-mcp"
]
}
}
}
For development/testing only:
docker compose up -d # Runs server in background for testing
Prerequisites:
# Clone the repository
git clone https://github.com/arvindand/maven-tools-mcp.git
cd maven-tools-mcp
# Quick build (CI-friendly - unit tests only)
./mvnw clean package -Pci
# Full build with all tests (requires network access)
./mvnw clean package -Pfull
# Run the JAR
java -jar target/maven-tools-mcp-2.0.5.jar
Claude Desktop configuration for JAR:
{
"mcpServers": {
"maven-tools": {
"command": "java",
"args": [
"-jar",
"/absolute/path/to/maven-tools-mcp-2.0.5.jar"
]
}
}
}
For easier builds, use the provided scripts in the build/ folder:
Linux/macOS:
cd build
./build.sh # Complete build helper
./build-docker.sh # Docker-focused helper
Windows:
cd build
build.cmd # Complete build helper
build-docker.cmd # Docker-focused helper
The server can be configured via application.yaml:
# Cache configuration
spring:
cache:
type: caffeine
# Maven Central Repository settings
maven:
central:
repository-base-url: https://repo1.maven.org/maven2
timeout: 10s
max-results: 100
# Logging (minimal for MCP stdio transport)
logging:
level:
root: ERROR
Blog Posts:
This project is licensed under the MIT License - see the LICENSE file for details.
Arvind Menon
Install via CLI
npx mdskills install arvindand/maven-tools-mcpMaven Tools MCP Server is a free, open-source AI agent skill. Dependency intelligence infrastructure for AI assistants and agents working with JVM projects. Traditional upgrade workflow: 1. Google "Spring Boot 2.7 to 3.2 migration guide" (5 minutes) 2. Read 50-page migration docs, cross-reference breaking changes (15 minutes) 3. Check Maven Central for compatible dependency versions (5 minutes) 4. Update pom.xml, compile, fix breaking changes (30 minutes) 5.
Install Maven Tools MCP Server with a single command:
npx mdskills install arvindand/maven-tools-mcpThis downloads the skill files into your project and your AI agent picks them up automatically.
Maven Tools MCP Server works with Claude Code, Claude Desktop, Cursor, Vscode Copilot, Windsurf, Continue Dev, Gemini Cli, Amp, Roo Code, Goose. Skills use the open SKILL.md format which is compatible with any AI coding agent that reads markdown instructions.