"Information about Java programming language" 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 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 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.

Enables AI coding agents to interact with TypeScript projects through compiler-level code intelligence, providing tools for navigation, type information, diagnostics, refactoring, and semantic search.

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.

TypeScript-based MCP server designed to enhance code editing experiences by providing features such as hover information, code completion, and diagnostics.

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.

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.

Enables Codex to read, write, and design windows for E-language (易语言) programs using official IDE APIs, allowing natural language control of development workflows.

Runs a language server and provides tools for communicating with it. Language servers excel at tasks that LLMs often struggle with, such as precisely understanding types, understanding relationships, and providing accurate symbol references.

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.

Exposes VSCode's Language Server Protocol features through MCP, enabling AI assistants to perform language-aware operations like symbol navigation, reference tracking, safe renaming, type information retrieval, and hover documentation across codebases.

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

Provides LLMs with comprehensive knowledge about the V programming language, including documentation access, code examples, standard library reference, and syntax explanations to help users learn and work with V.

Enables intelligent image analysis using GLM-4.5V model, specializing in extracting and analyzing code from screenshots with support for file paths and clipboard input.

Automates Java/Maven test generation, JaCoCo coverage analysis, and Checkstyle checking through MCP tools. Enables iterative improvement of test coverage and code style until quality goals are met.