A Frida MCP server to enable autonomous AI assistance for Android instrumentation
{
"mcpServers": {
"kahlo-mcp": {
"command": "<see-readme>",
"args": []
}
}
}No install config available. Check the server's README for setup instructions.
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A Frida MCP server to enable autonomous AI assistance for Android instrumentation
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Last commit 63 days ago. 87 stars.
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Transport: stdio. Works with Claude Desktop, Cursor, Claude Code, and most MCP clients.
No automated test available for this server. Check the GitHub README for setup instructions.
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Kahlo is a Frida MCP server that exposes Android dynamic instrumentation capabilities to AI agents. It wraps Frida's runtime manipulation APIs into a structured tool interface, enabling AI systems to attach to processes, inject instrumentation code, capture telemetry, and iterate on analysis workflows without manual intervention.
The server manages the full lifecycle of instrumentation sessions: device discovery, process attachment or spawning, job execution with per-script isolation, event streaming with cursor-based pagination, and binary artifact storage. Jobs run in isolated Frida scripts with automatic cleanup on cancellation. A built-in stdlib provides many standard code primitives for Java object inspection, stack traces, Intent parsing, and method hooking.
For more details, please read the accompanying blog post: https://knifecoat.com/Posts/Scalable+research+tooling+for+agent+systems
Edit the existing kahlo-mcp/config.json in this repo and set adbPath to the full path of the ADB binary you want to use. This works fine on Windows and UNIX systems.
{
"transport": "stdio",
"logLevel": "info",
"dataDir": "./data",
"adbPath": "/path/to/adb"
}
Install dependencies and build:
cd kahlo-mcp
npm install
npm run build
| Field | Required | Description |
|---|---|---|
transport | Yes | Only "stdio" is supported for now (SSE coming) |
logLevel | Yes | Use "info" for now (more controls coming) |
dataDir | Yes | Directory for runs, modules, drafts, artifacts. Defaults to kahlo-mcp/data |
adbPath | Yes | Full path to ADB binary |
Upload a frida-server binary to your device that matches the frida version in kahlo-mcp/package.json, then run it (ideally as root).
Edit your claude user json preference file (/mcp will show you where):
{
"mcpServers": {
"frida-kahlo": {
"type": "stdio",
"command": "node",
"args": [
"C:/Your/Full/Path/k4hlo/kahlo-mcp/dist/index.js"
],
"cwd": "C:/Your/Full/Path/k4hlo/kahlo-mcp"
}
}
}
Add to Cursor MCP settings (Preferences > Cursor Settings > Tools & MCP):
{
"mcpServers": {
"frida-kahlo": {
"command": "node",
"args": ["/Your/Full/Path/k4hlo/kahlo-mcp/dist/index.js"],
"cwd": "/Your/Full/Path/k4hlo/kahlo-mcp"
}
}
}
You will find your toml file under your user configuration folder for .codex:
model = "gpt-5.3-codex"
model_reasoning_effort = "medium"
[mcp_servers.frida-kahlo]
command = "node"
args = ["/Your/Full/Path/k4hlo/kahlo-mcp/dist/index.js"]
cwd = "/Your/Full/Path/k4hlo/kahlo-mcp"
This section is a quick, utilitarian map of the tool surface. For the complete operational contract (concepts, workflows, failure modes, and full stdlib reference), ask you AI to call kahlo_mcp_about to tell you more!
kahlo_mcp_about: returns the kahlo operational contract (tool inventory, concepts, workflows, failure modes, stdlib reference).kahlo_devices_list: list connected devices ready for instrumentation.kahlo_devices_get (device_id): detailed device info (model/version/transport) and readiness signals.kahlo_devices_health (device_id): health check for ADB + frida-server presence/running.kahlo_processes_list (device_id, scope?): list running processes (pid + name). Use this before `mod