TDA - Thread Dump Analyzer

[!IMPORTANT] 2026-01-30 - TDA 3.0 is now available! 🚀 This major release brings significant enhancements:

Extended MCP Server: Now with Carrier Thread Pinning detection and support for SMR (Safe Memory Relocation) parsing.

Modernized UI: Completely refreshed look and feel using FlatLaf.

Usability Improvements: Fixed several long-standing issues, including improved split-pane management and faster parsing.

Native macOS Support: Now providing a dedicated macOS binary.

Logging: Especially for troubleshooting in MCP Mode.

TDA (Thread Dump Analyzer) for Java is a powerful yet lightweight Swing GUI and MCP Server for analyzing Thread Dumps and Heap Information generated by the Java VM. It is designed to assist in diagnosing performance issues, deadlocks, and memory problems in Java applications. With its MCP capabilities, TDA can be seamlessly integrated into AI-powered development workflows.

TDA supports Java 1.4.x through Java 21+, including specialized support for Virtual Threads (Java 19+) and JSON-based thread dumps (Java 21+, experimental).

🚀 Key Features

Comprehensive Thread Dump Parsing: Parse multiple thread dumps from log files and display them in an intuitive tree structure.
Virtual Threads Support: Analyze virtual thread states, pinning issues, and carrier thread relationships.
Statistical Analysis: Detailed statistics about thread states, monitors, and waiting/locking threads.
Long-Running Thread Detection: Identify threads that persist across multiple dumps, helping find "hung" or inefficient code.
Deadlock Detection: Automatic identification of deadlocks with detailed monitor information.
JSON-based Thread Dumps: Support for parsing JSON-formatted thread dumps created with jcmd <pid> Thread.dump_to_file -format=json <file>. Note: JSON dumps currently provide basic information (name, tid, stack trace) but lack details like thread states or native IDs. For comprehensive analysis, textual dumps are recommended.
Class Histogram Analysis: Integrated analysis of heap objects (via -XX:+PrintClassHistogram).
Flexible Filtering & Categorization: Use custom filters and categories to manage large numbers of threads.
Model Context Protocol (MCP) Support: Headless analysis mode for integration with AI tools like Cursor, Junie, or Claude Desktop.
Session Management: Save and reopen analysis sessions.

📦 Installation

TDA can be installed or used in several ways:

1. Self-contained JAR

The simplest way to use TDA is to download the tda.jar from the Releases page. Since it's a self-contained JAR, you can run it directly on any system with Java installed.

2. VisualVM Plugin

TDA is available as a plugin for VisualVM. You can install it directly from the VisualVM Plugin Center (Tools -> Plugins). This integrates TDA's analysis capabilities directly into your VisualVM environment. The last available version in the plugin center is 2.4. For installing the recent version, download the three nbm files from the Releases page and install them manually in the VisualVM plugins settings.

3. macOS Binary (DMG)

TDA provides a native macOS DMG package for easier installation. This package includes its own Java runtime, so no separate Java installation is required to run it.

Installation (Unsigned Binary)

Since the DMG is currently unsigned, macOS will block it by default with a warning that the "developer cannot be verified". To install and run TDA:

Download the TDA-<version>.dmg from the Releases page.
Open the DMG and drag TDA.app to your Applications folder.
Right-click (or Control-click) on TDA.app in your Applications folder and select Open.
A dialog will appear asking if you are sure you want to open it. Click Open.
After this first launch, you can open TDA normally from Launchpad or Spotlight.

If this fails, you might need to confirm the installation from the system settings. For this just follow the instructions after clicking on the '?' Icon on the upper right corner of the error dialog.

🛠 Usage Modes

TDA can be used in four different ways to suit your environment:

1. Standalone Application

The most common way to use TDA for offline analysis of production log files. (See Installation for how to get the JAR).

How to run:

java -Xmx512m -jar tda.jar

Once opened, you can add log files containing thread dumps. TDA will parse them and display them in the tree view. If you have several or big log files, you might need to adjust the memory allocation by increasing the -Xmx flag.

Important Features:

Thread Dump Tree: Navigate through all detected dumps in a log file.
Regular Expression for Timestamps: Since thread dumps often lack timestamps, you can provide a regex to correlate dumps with log entries.
Find Long Running Threads: Compare consecutive dumps to see which threads haven't moved.
Filters & Categories: Group threads by name or state to reduce noise.
Monitors & Deadlocks: Detailed view of monitor usage and automatic deadlock detection.

2. JConsole Plugin

Analyze a running application directly within JConsole. This is particularly useful for production environments where you need to quickly inspect the state of a live system. The plugin is part of the standalone jar, which needs to be downloaded for this.

How to use: Start JConsole with the TDA plugin:

jconsole -pluginpath tda.jar

A new "Thread Dumps" tab will appear. From here, you can:

Request Thread Dumps: Generate fresh dumps directly from the connected JVM.
Analyze in Real-time: Use all of TDA's parsing and analysis features on the live data.

3. VisualVM Plugin

TDA is also available as a plugin for VisualVM. You can install it directly from the VisualVM Plugin Center. It provides the same powerful analysis tools integrated into your VisualVM workflow. (See Installation for details).

4. JSON-based Thread Dumps (Experimental)

Newer JDK versions allow generating thread dumps in JSON format using:

jcmd <pid> Thread.dump_to_file -format=json <file>

TDA can parse these files, but please note that the information provided in the JSON format is currently more basic than the traditional textual output. For example, it often lacks detailed thread states (like MONITOR_WAIT vs WAITING) or native thread IDs (nid).

Recommendation: For in-depth analysis of deadlocks, monitor contention, or specific thread states, the textual thread dump format is still preferred.

5. MCP Server (Headless Analysis)

For integration with AI tools (like Cursor, Junie, or Claude Desktop) or automated pipelines, TDA can run as a Model Context Protocol (MCP) server. This allows for headless analysis of thread dumps using AI assistance. You need to download the standalone jar for this.

There's also an articel on dev.to about how to use TDA as an MCP server.

How to run:

java -Djava.awt.headless=true -jar tda.jar --mcp

Integration Example (Junie/Cursor):

{
  "mcpServers": {
    "tda": {
      "command": "java",
      "args": ["-Djava.awt.headless=true", "-jar", "path/to/tda.jar", "--mcp"]
    }
  }
}

Available Tools

The MCP server exposes the following tools:

| Tool | Arguments | Description | |:--------------------|:--------------------------|:-----------------------------------------------------------------------------------------------| | parse_log | path (string, required) | Parses a log file containing Java thread dumps. This must be the first action for a log file. | | get_summary | None | Returns a summary of all parsed thread dumps (index, name, timestamp, thread/deadlock counts). | | check_deadlocks | None | Checks for and returns information about any deadlocks detected in the parsed thread dumps. | | find_long_running | None | Identifies threads that remain in the same state/stack trace across consecutive dumps. | | analyze_virtual_threads | None | Detects virtual threads wher

Tda

Install / Use

README