ghidralens

GhidraLens

Ghidra, rendered inside your AI client. Click a symbol to rename it. Click a call to follow it.

Every Ghidra MCP server so far returns text. The model can read it; you cannot navigate it. GhidraLens returns the same analysis as an interactive view — built on MCP Apps (io.modelcontextprotocol/ui), the extension that lets a server ship real HTML into the conversation.

You and the model are looking at the same live program. Rename a variable by clicking it and the model's next decompile sees the new name.

That is a real screenshot: where.exe, decompiled by Ghidra, every identifier carrying the address it came from.

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What you get

View	What it does
Decompiler	Ghidra's C output as a live token stream — every identifier carries its address and its kind. Click a local to rename it, click a call to follow it. Callers, callees and variables in a sidebar.
Function browser	Every function in the binary, filterable and sortable by address, name, size or caller count. Click a row to decompile it.
Call graph	Callers to the left, callees to the right, the function you asked about in the middle. Click any node to recenter.

Function browser

Call graph

Ten tools total. Three open views; the rest are lookups and writes, including two the model never sees — they exist only so a click in a view can fire them.

How it fits together

  MCP client  ──stdio──▶  server/  ──HTTP──▶  bridge/  ──JPype──▶  Ghidra (JVM)
  (Claude,                 TypeScript         PyGhidra            program stays
   Cursor, …)              MCP server         session             resident
       ▲
       │  ui:// HTML in a sandboxed iframe
       └──  ui/  three self-contained views

The bridge is a separate long-lived process on purpose. Ghidra's auto-analysis is the expensive step, and it happens once. Measured on a 64 KB Windows system utility (198 functions):

First open, with analysis	25 s
Re-open the same binary	0.3 s
Decompile one 2 KB function	0.4 s
87-node call graph	< 0.1 s

Restart the MCP server or the client and the analysed program is still there.

Setup

Prerequisites: Ghidra 11.3+, a JDK 21+, Python 3.9–3.13 (not 3.14 — JPype has no wheel for it yet), Node 20+. See bridge/setup.py.md — the Python side is fussy and that file covers every way it goes wrong. GhidraLens finds a JDK for you if JAVA_HOME is unset, which covers the usual "installed Java, shell has not restarted" case.

git clone https://github.com/hellosverre/ghidralens
cd ghidralens
npm install
npm run build

Then start the bridge on the binary you want to look at:

python bridge/serve.py --binary /path/to/target.exe

It prints a token. Put that, and the path to the built server, into your MCP client config:

{
  "mcpServers": {
    "ghidralens": {
      "command": "npx",
      "args": ["-y", "ghidralens"],
      "env": {
        "GHIDRALENS_BRIDGE_URL": "http://127.0.0.1:8799",
        "GHIDRALENS_TOKEN": "paste-the-printed-token-here"
      }
    }
  }
}

Running from a clone instead? Point command at node and args at /absolute/path/to/ghidralens/server/dist/index.js.

Then ask your client: "decompile the function that handles license validation".

Also listed in the official MCP Registry as io.github.hellosverre/ghidralens.

Editing the config by hand? Quit the client first — properly, including any system-tray icon. Claude Desktop keeps its own copy of claude_desktop_config.json in memory and writes it back over yours when it exits, so an edit made while it is running silently disappears on the next restart. Editing through Settings → Developer → Edit Config avoids the race entirely.

Tools

Tool	Visible to	Renders
open_binary	model	—
program_info	model	—
decompile	model + view	Decompiler
list_functions	model + view	Function browser
call_graph	model + view	Call graph
find_strings	model	—
xrefs_to	model + view	—
rename_symbol	model + view	—
add_comment	view only	—
save_program	model	—

add_comment is hidden from the model deliberately. Visibility is how MCP Apps separates "the agent may do this" from "a click may do this"; keeping write tools out of the model's list keeps it short and stops the model from renaming things on its own initiative.

Renames and comments live in memory until save_program writes them into the Ghidra project — after which they show up in the Ghidra GUI like any other edit.

Developing the views without Ghidra

npm run dev:ui
# open http://localhost:5173/dev/harness.html

ui/dev/harness.ts is a real MCP Apps host — it runs the SDK's AppBridge against the view in an iframe, so the ui/initialize handshake, the opening ui/notifications/tool-result, and every tools/call a click fires all go over real postMessage JSON-RPC. There is a message trace down the right-hand side and a host-theme switch, because the views have to look right in both.

Two data sources, switchable in the toolbar:

• fixtures — no Ghidra needed, nothing to install
• live bridge — proxies to a running bridge, so you develop against a real analysed program

Use live before you trust anything. Fixtures are tidy; real output is a 400-line function with 56 locals and an 87-node call graph, and that is where layout actually breaks.

Running it on a local model

Reverse engineering is exactly the work people would rather not send to a hosted model, so agent/ollama-agent.mjs is a small MCP host that puts GhidraLens behind Ollama instead. No API key, nothing leaves the machine.

OLLAMA_MODEL=qwen3:14b node agent/ollama-agent.mjs "what does this binary do?"

It respects _meta.ui.visibility, so the app-only tools stay hidden from the model — the same separation a graphical client enforces. A ~9B model is enough to orient itself with find_strings and list_functions; a 14B is noticeably better at reading decompiled C.

Tests

Suite	Needs Ghidra	Covers
node server/smoke.mjs	no	MCP surface: tools, ui:// resources, tool visibility, graceful failure with no bridge
python bridge/test_serve.py	no	Bridge auth, CSRF rejection, routing, input validation
python bridge/test_session.py	yes	Every Ghidra call: analysis, caching, decompiler tokens, imports, renames, writes
node server/live.mjs	yes (bridge running)	The whole chain, and that every payload matches the shape the views index into

The first two are what CI can run. test_session.py is the one that matters after touching bridge/session.py — it is the only thing that proves the Ghidra API calls are right, and it caught three real bugs the day it was written.

Containerising it

The Dockerfile builds the server and views for clients or registries that want to start it themselves. One thing to get right:

CMD ["node", "server/dist/index.js"]     # correct
CMD ["npm", "run", "start"]              # breaks the protocol

A stdio MCP server speaks JSON-RPC on stdout, and npm run / pnpm run print the script banner there first:

> ghidralens@0.1.1 start
> node server/dist/index.js

Those lines land in the stream ahead of the handshake and the client gives up mid-initialize. The symptom is unhelpful - the container builds, starts, exits cleanly, and the client just reports no tools - so it is worth not stepping on. Invoke node directly.

Real analysis still needs the bridge on the host: 127.0.0.1 inside a container is the container, so point GHIDRALENS_BRIDGE_URL at host.docker.internal or a real address.

Security

The bridge binds 127.0.0.1 only, requires a per-run token in X-GhidraLens-Token, and rejects any request carrying an Origin or Referer header — so a page open in your browser cannot reach your decompiler. It has no multi-user model and is not meant to be exposed; --host refuses anything but loopback.

Analysing a binary does not execute it, but Ghidra will happily open malware. Use the same isolation you would use for any other RE work.

Licence

MIT.