pzalutski-pixel/javalens-mcp
by Various
MCP server providing semantic Java code analysis for AI agents. Built on Eclipse JDT with tools for navigation, refactoring, search, and metrics.
MCP
pzalutski-pixel/javalens-mcp
Added 1 June 2026
Overview
This is an MCP server that gives AI agents semantic understanding of Java codebases using Eclipse JDT. It exposes tools for navigation, refactoring, search, and metrics so agents can perform structured code analysis.
Best for
Best for
Java developers building or extending AI-assisted coding workflows that need deep code understanding.
Use cases
- AI-assisted code navigation and class hierarchy exploration
- Automated Java refactoring suggestions within an MCP-aware editor
- Semantic search across a Java project for symbols and references
How to use
Install
npx -y javalens-mcp Tools exposed
search_symbolsgo_to_definitionfind_referencesfind_implementationsget_type_hierarchyget_document_symbolsget_symbol_infoget_type_at_positionget_method_at_positionget_field_at_positionfind_annotation_usagesfind_type_instantiationsfind_castsfind_instanceof_checksfind_throws_declarationsfind_catch_blocksfind_method_referencesfind_type_argumentsfind_reflection_usageget_diagnostics
Tested with
Claude Code, Cursor
Example client config
{\n "mcpServers": {\n "javalens": {\n "command": "java",\n "args": ["-jar", "/path/to/javalens/javalens.jar", "-data", "/path/to/javalens-workspaces"],\n "env": {\n "JAVA_PROJECT_PATH": "/path/to/your/java/project"\n }\n }\n }\n} Notes
This is an MCP server that gives AI agents semantic understanding of Java codebases using Eclipse JDT. It exposes tools for navigation, refactoring, search, and metrics so agents can perform structured code analysis.
29 stars on GitHub. Last updated 2026-05-29. Licensed MIT.
Use cases
- AI-assisted code navigation and class hierarchy exploration
- Automated Java refactoring suggestions within an MCP-aware editor
- Semantic search across a Java project for symbols and references
Pros
- Leverages Eclipse JDT for accurate Java semantics
- Provides concrete analysis tools directly to AI agents
- Lightweight integration via MCP protocol
Cons
- Limited to Java projects only
- Small community and low star count may mean less support
- Requires MCP-compatible client or agent setup
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- Leverages Eclipse JDT for accurate Java semantics
- Provides concrete analysis tools directly to AI agents
- Lightweight integration via MCP protocol
Cons
- Limited to Java projects only
- Small community and low star count may mean less support
- Requires MCP-compatible client or agent setup
Pairs with
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