MCP SERVERProfile a running JVM or a snapshot from your coding assistant — CPU hotspots, JDBC and HTTP calls, and heap analysis.
LABSCO SUMMARY
The expansion pattern is what makes it work in a conversation: hotspots come back with ids, and expanding one returns its back trace or children, so the model can walk down a call tree the way a person would click through it. Attaching to a JVM inside a Docker container is supported directly, which is where most of the awkwardness usually is.
JProfiler's own MCP server. It starts a profiling session, attaches to a live JVM or loads an existing snapshot, and hands the recorded data back as structured hotspots and heap summaries an assistant can drill into — CPU, JDBC, JPA, HTTP server and client, and MongoDB.
A JProfiler license — on first use you can start a free 10-day evaluation or enter an existing key. Clients run npx -y @ej-technologies/jprofiler-mcp over stdio. Optional startup arguments narrow what is exposed: --filter takes tool name patterns, --subsystems limits which subsystems are enabled, and --max-depth caps call tree expansion.
One command — npx -y @ej-technologies/jprofiler-mcp
USE CASES
List the JVMs, attach to the right one, and walk down from the top hotspot.
Load the file, wait for it to be ready, and read the recorded subsystems.
Dump the heap, then read the biggest objects and the retained class list.
Get the JVM parameter to add to the process for the subsystems you want recorded.
Record with the JDBC subsystem enabled and expand the hotspots it produces.
Counted once with only startup setup. Other versions, modes or optional credentials may expose a different list.
attachAttach to a running JVM by PID or to a JVM inside a Docker container. Records performance data for the following subsystems: cpu, jdbc, jpa, http_server, http_client, mongo_db until check_status is called with 'stopRecording: true'. You can use list_jvms to discover JVMs.
check_statusCheck the status of a profiling session and discover which subsystems were recorded. Can be used to stop recording. When the data is ready provides information about the recorded subsystems (e.g. cpu, jdbc, http_client).
create_heap_dumpDump the heap of a locally running JVM or of a JVM inside a Docker container to an HPROF file and load it for analysis. You must call check_status to check the loading progress and - after this returns 'data_ready' - retrieve data with get_heap_data. You can use list_jvms to discover JVMs.
expand_performance_hotspotExpand a performance hotspot or call tree. Returns a JSON object containing the back trace of a hot spot or the children of the call tree node with the provided id.
get_heap_dataRetrieve aggregated data of a heap dump. Can show biggest objects, retained class lists, and class histograms.
get_performance_hotspotsRetrieve a sorted list of top performance hotspots. Returns a JSON object containing an array of hotspots with timing data and expansion ids. Use expand_performance_hotspot to drill down.
list_jvmsList locally running JVMs or JVMs running in a Docker container with their command line and PID. Use the information to call attach or create_heap_dump.
load_snapshotLoad an existing .jps, .hprof, or .jfr file for analysis. You must call check_status to check the loading progress and - after this returns 'data_ready' - retrieve data with the get_* tools. To compare two performance snapshots, load both in a single call: set filePath to the run to analyze and baselineFilePath to the baseline; The get_performance_hotspots tool then report the differences.
prepare_profilingPrepare a profiling session by retrieving a JVM parameter to be added to a Java process call to record performance data with stack traces for the following subsystems: cpu, jdbc, jpa, http_server, http_client, mongo_db. The result is a JSON object containing a 'jvmParameter' to be added (quoted) to the Java process at startup. Add it verbatim and do not create or modify any files; the configuration is already set up for you. Run the Java process; the recording is saved automatically when the JVM terminates (or after the optional maximum duration). Then call check_status to access the data - do not load the saved snapshot file yourself.
BEFORE YOU INSTALL
9 tools when we connected.
npx on PATH
CONTEXT COST
Rated by the size of the 9 tool definitions that ride along on every turn, not by how many there are
INSTALLATION
Add the block to your client, or run the command directly.
{
"mcpServers": {
"jprofiler-mcp": {
"command": "npx",
"args": [
"-y",
"@ej-technologies/jprofiler-mcp"
]
}
}
}npx -y @ej-technologies/jprofiler-mcpMCP servers
ChromeDevTools
mrexodia
tidewave-ai
MxIris-Reverse-Engineering