Javalens MCP is an MCP server by Pzalutski Pixel, designed for deep semantic Java code analysis. Built with Java and requiring Java 21+, it uses Eclipse JDT for its core functionality. It enables AI assistants and agents to understand complex Java codebases, providing essential insights for AI-driven development. Distributed via GitHub Releases as javalens.zip or javalens.tar.gz, or through npm using npx javalens-mcp, it offers a rich set of analysis tools.

What does Javalens MCP connect to?

Javalens MCP connects to Java codebases to provide deep semantic analysis for AI assistants. It functions specifically as an MCP server for Java code analysis. The server runtime requires Java 21 or later, and its core functionality is built upon Eclipse JDT. This architecture allows it to effectively interact with and interpret Java projects, making it a foundational tool for AI-powered code understanding and manipulation workflows.

Core features and tools

Javalens MCP exposes approximately 75 semantic analysis tools. Specific callable tools include load_project, search_symbols, find_references, analyze_type, get_diagnostics, get_call_hierarchy_incoming, and rename_symbol. It also supports operations such as identifying annotation usages, type instantiations, reflection usage, and finding possible bugs. These capabilities enable detailed interaction with Java code, supporting both analysis and refactoring operations like extract_method.

Who is it for?

Javalens MCP targets AI assistants, AI agents, and developers working on larger Java codebases, particularly those using AI coding assistants such as Claude Code. It supports compiler-accurate semantic analysis of Java code and precise identification of references, implementations, and call hierarchies. It also distinguishes between field reads and writes. This tool is essential for those integrating advanced AI capabilities into Java development. Skip this if your development workflow does not involve Java code or AI assistant integration.

How to get started

Access is granted through per-connection authentication using API keys and secrets during the MCP handshake process. It supports stdio and Streamable HTTP transport protocols.

Use cases

• Performing compiler-accurate semantic analysis of Java code to inform AI assistants.
• Precisely finding references, implementations, and call hierarchies, and distinguishing field reads from writes.
• Executing refactoring operations, including renaming symbols and extracting methods within Java projects.

Javalens MCP delivers deep semantic analysis for Java codebases, serving AI assistants and developers. It uses Eclipse JDT and provides a rich set of tools for code understanding and refactoring. Compatible with various AI clients, it integrates into modern AI-driven development workflows, enhancing code interaction.