JavaLens: AI-First Code Analysis for Java

An MCP server providing 75 semantic analysis tools for Java, built directly on Eclipse JDT for compiler-accurate code understanding.

Built for AI Agents

JavaLens exists because AI systems need compiler-accurate insights that reading source files cannot provide. When an AI uses grep or Read to find usages of a method, it cannot distinguish:

• A method call from a method with the same name in an unrelated class
• A field read from a field write
• An interface implementation from an unrelated class
• A cast to a type from other references to that type

This leads to incorrect refactorings, missed usages, and incomplete understanding of code behavior.

Compiler-Accurate Analysis

JavaLens provides compiler-accurate code analysis through Eclipse JDT—the same engine that powers Eclipse IDE. Unlike text search, JDT understands:

• Type resolution across inheritance hierarchies
• Method overloading and overriding
• Generic type arguments
• Import resolution and classpath dependencies
• Java source from version 1.1 through Java 25 (markdown Javadoc, module imports, compact source files, flexible constructor bodies)
• Lombok-generated members — a bundled agent makes @Data accessors and the like resolve, so code using them is not flagged as undefined

Example: Finding all places where UserService.save() is called:

AI Training Bias Warning

⚠️ Important for AI developers and users

AI models may exhibit trained bias toward native tools (Grep, Read, LSP) over MCP server tools, even when semantic analysis provides better results. This happens because:

1. Training data contains extensive grep/text-search patterns
2. Native tools are "always available" in the model's experience
3. The model may not recognize when semantic analysis is superior

To get the best results:

Add guidance to your project instructions or system prompt (e.g., CLAUDE.md for Claude Code):

## Code Analysis Preferences

For Java code analysis, prefer JavaLens MCP tools over text search:
- Use `find_references` instead of grep for finding usages
- Use `find_implementations` instead of text search for implementations
- Use `analyze_type` to understand a class before modifying it
- Use refactoring tools (rename_symbol, extract_method) for safe changes

Semantic analysis from JDT is more accurate than text-based search,
especially for overloaded methods, inheritance, and generic types.

What is JavaLens?

JavaLens is an MCP server that gives AI assistants deep understanding of Java codebases. It provides semantic analysis, navigation, refactoring, and code intelligence tools that go beyond simple text search.

Why Not LSP?

Language Server Protocol was designed for IDE autocomplete and basic navigation—not for AI agent workflows that require deep semantic analysis.

JavaLens wraps Eclipse JDT Core directly via OSGi, providing:

• Fine-grained reference types: Find specifically casts, annotations, throws clauses, catch blocks, instanceof checks, method references, type arguments
• Read vs write access distinction: Track where fields are mutated vs just read
• Indexed search: JDT pre-builds an index at load time, so symbol/reference queries do not walk source files
• Full AST access: Direct manipulation for complex refactorings

Installation

Prerequisites

• Java 21 or later (must be on PATH or set JAVA_HOME) — required for both install paths.
• Node.js 18+ — required only if you use the npm/npx install path below. Skip if you use the direct-download path.

JavaLens is an analytical server, not a compiler. It uses Eclipse JDT 2025-12 to parse and understand Java source code from version 1.1 through 25. Java 21 is required only as the server runtime.

Install from GitHub Releases (recommended — Java only)

This is the simplest path if you already have Java 21 and don't have Node.js. Download from Releases:

Extract to a location of your choice (e.g., /opt/javalens or C:\javalens). Then point your MCP client at the bundled jar — see Configure MCP Client below.

Install via npm (requires Node.js 18+)

If you already have Node.js, npx will download and cache the JavaLens distribution (~23 MB) on first run:

{
  "mcpServers": {
    "javalens": {
      "command": "npx",
      "args": ["-y", "javalens-mcp"],
      "env": {
        "JAVA_PROJECT_PATH": "/path/to/your/java/project"
      }
    }
  }
}

Configure MCP Client

Add to your MCP configuration (e.g., .mcp.json for Claude Code):

{
  "mcpServers": {
    "javalens": {
      "command": "java",
      "args": ["-jar", "/path/to/javalens/javalens.jar", "-data", "/path/to/javalens-workspaces"]
    }
  }
}

The -data argument specifies where JavaLens stores its workspace metadata. See How Workspaces Work below.

Auto-Load a Project

Set JAVA_PROJECT_PATH to auto-load a project when the server starts:

{
  "mcpServers": {
    "javalens": {
      "command": "java",
      "args": ["-jar", "/path/to/javalens/javalens.jar", "-data", "/path/to/javalens-workspaces"],
      "env": {
        "JAVA_PROJECT_PATH": "/path/to/your/java/project"
      }
    }
  }
}

Note: Project loading happens asynchronously in the background. The MCP server responds immediately while the project loads. Use health_check to monitor loading status—it will show "project.status": "loading" until complete, then "loaded" when ready.

How Workspaces Work

Unlike in-memory code models, Eclipse JDT requires a workspace directory to store:

• Search indexes for fast symbol lookup
• Compilation state and caches
• Project metadata

Workspaces Are Outside Your Source

JavaLens creates its workspace outside your source project to keep your codebase clean:

Your Java Project (unchanged)
├── src/main/java/
├── pom.xml
└── (no Eclipse files added)

JavaLens Workspace (specified by -data)
└── {session-uuid}/
    ├── .metadata/          <- JDT indexes and state
    └── javalens-project/   <- Links to your source (not copies)

Why this matters:

1. No pollution: Your source tree stays clean—no .project or .classpath files
2. No conflicts: Works alongside any build system without interference
3. Session isolation: Each MCP session gets its own workspace, enabling concurrent analysis

Session Lifecycle

1. JavaLens starts and creates a unique workspace: {base}/{uuid}/
2. load_project creates linked folders pointing to your source
3. JDT builds indexes in the workspace (not in your project)
4. When the session ends, the workspace is cleaned up

Tools

Navigation (10 tools)

Fine-Grained Reference Search (9 tools)

These use JDT's unique reference type constants—not available through LSP:

Analysis (20 tools)

Compound Analysis (4 tools)

Combine multiple queries to reduce round-trips:

Refactoring (16 tools)

All refactoring tools return text edits (and new-file content where a refactoring creates one) rather than applying changes directly:

Quick Fixes (5 tools)

Metrics (5 tools)

Project & Infrastructure (6 tools)

Usage

Basic Workflow

1. load_project(projectPath="/path/to/java/project")
2. search_symbols(query="*Service", kind="Class")
3. find_references(filePath="...", line=10, column=15)
4. analyze_type(typeName="com.example.UserService")

Coordinate System

All line/column parameters are zero-based:

• Line 0, Column 0 = first character of file

Path Handling

• Response paths are relative by default
• All paths use forward slashes for cross-platform consistency
• Input paths can be relative or absolute

Important Notes

Disk Synchronization

Every answer is verified against the files on disk at query time. Before any tool logic runs, JavaLens content-hashes the known source files, detects edits, additions, and deletions (the agent reports nothing — the server discovers changes itself), repairs exactly what changed, waits for the search index to absorb the repair, and only then answers. There is no file watcher and no background thread — verification is synchronous inside the query the agent issued, so there are no race conditions.

The agent's loop is just: edit → query.

1. Use JavaLens tools to analyze
2. Write changes to files
3. Use JavaLens tools to verify — answers already reflect the changes

load_project is needed only on first use, when a response reports RELOAD_REQUIRED (a build file like pom.xml changed, so the classpath must be rebuilt), or to rebuild everything from scratch. If verification itself fails, the query returns VERIFICATION_FAILED rather than an unverified answer.

Cost: verification is hash-based and parallel — measured per query at ~2 ms for a 72-file project, ~25 ms at 1,000 files, ~180 ms at 10,000 files. Repairs cost only what changed (one edited file reconciles in well under a second), never a full reindex.

Manual mode: set JAVALENS_DISK_SYNC=manual to restore the pre-1.5.0 contract — answers reflect the last load and the agent calls load_project after editing files. Tool descriptions and the MCP instructions field always state the active contract, and health_check reports it as diskSync.

Refactoring Returns Edits

Refactoring tools return text edits but don't modify files. This gives visibility into what would change before applying.

Session Isolation

Each MCP session is independent with its own workspace UUID. Multiple sessions can analyze the same project concurrently.

Build System Support

JavaLens loads three real build systems plus plain Java directories. Each is exercised end-to-end in CI against synthetic real-shaped fixtures (multi-module reactors with cross-module deps, real external libraries, annotation processors).

Subprocess invocations of mvn / gradle happen during project load. If a tool is missing or fails, JavaLens surfaces a structured LoadWarning (e.g. MAVEN_SUBPROCESS_FAILED, GRADLE_SUBPROCESS_FAILED, COMPLIANCE_LEVEL_UNKNOWN) in the load_project response so callers know analysis quality is degraded rather than silently getting an empty classpath.

Configuration

Building from Source

git clone https://github.com/pzalutski-pixel/javalens-mcp.git
cd javalens-mcp
./mvnw clean verify

Distributions are output to org.javalens.product/target/products/.

Build Requirements

• Java 21+ (server runtime)
• Maven 3.9+ (wrapper included as ./mvnw)

To run the full test suite (which includes end-to-end tests against real Maven, Gradle, and Bazel builds), the corresponding tools must also be on PATH:

• Maven (provided by the wrapper)
• Gradle 8+
• Bazel 9+ (or bazelisk)

Tests gracefully skip when a tool is missing on a developer machine. Set JAVALENS_TESTS_REQUIRE_TOOLS=true to flip the gate: missing tools cause a hard failure instead of a skip. CI runs with this flag set so any provisioning gap surfaces as a real failure rather than weakening the suite silently.

Testing

# Full suite, gentle (missing tools skip)
./mvnw verify

# Full suite, strict (missing tools fail; what CI does)
JAVALENS_TESTS_REQUIRE_TOOLS=true ./mvnw verify

Build-system coverage is structured as focused per-bug tests plus realistic end-to-end tests. The end-to-end tests load a single representative project per build system that exercises every fix in one pass — multi-module Maven with Lombok APT and cross-module references; multi-project Gradle with annotation processors; multi-target Bazel with cross-target deps. CI runs them on Linux, macOS, and Windows.

Architecture

flowchart TD
    Client["<b>MCP Client</b>"]
    MCP["<b>org.javalens.mcp</b><br/>McpProtocolHandler → ToolRegistry → 75 Tools"]
    Core["<b>org.javalens.core</b><br/>JdtServiceImpl → WorkspaceManager, SearchService"]
    JDT["<b>Eclipse JDT Core</b> (via OSGi / Equinox)<br/>IWorkspace, IJavaProject, SearchEngine, ASTParser"]

    Client -->|"JSON-RPC over stdio"| MCP
    MCP --> Core
    Core --> JDT

License

MIT License - see LICENSE for details.