"A repository for hosting and sharing Java libraries" matching MCP servers:

Decompiles Maven dependencies into readable Java source directly inside your AI agent.

Provides Java development capabilities through Eclipse JDT.LS, enabling symbol navigation, code diagnostics, workspace searching, and Javadoc access across Java projects.

Enables natural-language analysis of GitHub repositories by exposing repository metadata, source code retrieval, search, and file reading as MCP tools, with answers grounded in the actual repository content.

Enables automatic generation of explicit boilerplate methods (getter/setter/equals/hashCode/toString/constructors) for Java entity classes, replacing Lombok dependencies with visible, reviewable code.

Enables AI to analyze Java classes from Maven project dependencies, with support for decompilation strategies and configurable groupId mappings.

An MCP server that uses the CFR decompiler to convert Java .class and .jar files back into readable source code. It supports single-file, batch, and recursive directory decompilation with automated CFR tool management.

A high-performance MCP server that bridges AI agents with Java codebases, providing professional-grade Java language intelligence via Eclipse JDT.LS.

A Codex MCP server that provides low-token Java semantic navigation using source indexing and optionally JDT Language Server for enhanced symbol, references, and diagnostics.

Provides intelligent codebase analysis, dependency scanning, architecture detection, security vulnerability scanning, and automatic documentation generation for modern development teams.

Enables coding agents to perform Java semantic navigation, diagnostics, compilation, JUnit testing, debugging, coverage, affected-test discovery, and safe refactoring previews without modifying source files.

Enables AI assistants to look up Java class definitions and list dependencies from Maven projects by analyzing local JAR files via the Model Context Protocol.

Bridges agentic coding tools and live Java runtime behavior through a lightweight sidecar agent. Attaches directly to a running JVM to provide bytecode-level runtime signals for probe-verified inspection and deterministic debugging.

Enables AI tools to analyze Java dependencies by scanning Maven projects, decompiling JAR files, and extracting detailed class information including methods, fields, and inheritance relationships. Solves the problem of AI hallucinations when generating code that calls external dependencies by providing accurate class structures through decompilation.

Decompiles Java .class files, packages, and JARs into readable source code, enabling AI assistants to inspect Java bytecode.

A Model Context Protocol server that helps programmers understand code by providing explanations, tech stack analysis, and best practice suggestions through prompt templates.

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

Turns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.

Enables MCP-compatible LLM clients to retrieve repository metadata, list and read files, search code, and fetch README content through natural-language requests.

MCP server that inspects Git repository changes, runs optional validation commands, and generates Markdown reports. Exposes a review_repository tool for AI clients to analyze repositories.

Enables AI agents to inspect Git repositories for changes across committed, staged, unstaged, and untracked scopes, and run allowlisted validation commands with structured results.