MCP server that gives AI agents instant access to decompiled Java code from Maven/Gradle dependencies. Blazing fast Java class analysis for Claude
MCP server that gives AI agents instant access to decompiled Java code from Maven/Gradle dependencies. Blazing fast Java class analysis for Claude, Cursor, and other LLM-powered IDEs. Exposed over MCP by the jarp mcp mcp server, that capability becomes something an assistant can invoke while it works, not something you go and do afterwards.
That's it. No installation. No configuration. CFR decompiler bundled.
Everything the assistant can do here goes through one of these:
Impact — The Impact tool exposed by this serverInstallation goes through your MCP client rather than a global install: point it at jarp-mcp on npm and it is fetched when the client starts. The copy-paste blocks for Claude Desktop, Claude Code and Cursor are further down this page.
Among the AI and media services options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Jarp MCP's toolset — Impact — is a fair guide to whether it matches your workflow. It is maintained by terseprompts; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against Jarp MCP's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| Impact | The Impact tool exposed by this server. |
{
"mcpServers": {
"jarp-mcp": {
"command": "npx",
"args": ["-y", "jarp-mcp"]
}
}
}Configuration as documented by the project. Restart the client after saving.
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.