By stanfrbd
Extract, enrich, and analyze Indicators of Compromise (IOCs) and observables using Cyberbro threat intelligence toolkit, running locally as an MCP stdio subprocess connected to a configurable Cyberbro service.
npx claudepluginhub stanfrbd/mcp-cyberbro
Extract IoCs from messy text and analyze them with Cyberbro.
🌐 demo.cyberbro.net
Model Context Protocol server for Cyberbro.
This project is packaged as a standard Python distribution and can be launched with:
uvx mcp-cyberbropip install mcp-cyberbro then mcp-cyberbrostdio, sse, or streamable-http transports.uvx (standalone)uvx mcp-cyberbro --cyberbro_url http://localhost:5000
pippip install mcp-cyberbro
mcp-cyberbro --cyberbro_url http://localhost:5000
pip install -e .
mcp-cyberbro --cyberbro_url http://localhost:5000
Default container command starts in streamable-http mode on port 8000.
docker run --rm -p 8000:8000 \
-e CYBERBRO_URL=http://host.docker.internal:5000 \
ghcr.io/stanfrbd/mcp-cyberbro:latest
To force stdio transport:
docker run -i --rm \
-e CYBERBRO_URL=http://host.docker.internal:5000 \
ghcr.io/stanfrbd/mcp-cyberbro:latest \
--transport stdio
Copy .env.example and set at least:
CYBERBRO_URL (required)Supported environment variables:
CYBERBRO_URLAPI_PREFIX (default: api)SSL_VERIFY (true/false)MCP_TRANSPORT (stdio, sse, streamable-http)MCP_HOSTMCP_PORTMCP_MOUNT_PATHMCP_SSE_PATHMCP_STREAMABLE_HTTP_PATHCLI flags are also available and override env values.
You can use this server with Claude Desktop, Claude Code, Cursor, OpenAI-compatible MCP clients, or any other MCP client.
Example config using uvx:
{
"mcpServers": {
"cyberbro": {
"command": "uvx",
"args": ["mcp-cyberbro"],
"env": {
"CYBERBRO_URL": "http://localhost:5000"
}
}
}
}
Example with Docker + stdio:
{
"mcpServers": {
"cyberbro": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-e",
"CYBERBRO_URL",
"ghcr.io/stanfrbd/mcp-cyberbro:latest",
"--transport",
"stdio"
],
"env": {
"CYBERBRO_URL": "http://localhost:5000"
}
}
}
}
Create .vscode/mcp.json
{
"servers": {
"mcp-cyberbro": {
"type": "stdio",
"command": "uvx",
"args": [
"mcp-cyberbro"
],
"env": {
"CYBERBRO_URL": "http://127.0.0.1:5000"
}
}
}
}
server.json is included for MCP Registry publication and points to PyPI package mcp-cyberbro.
Release-created workflows:
.github/workflows/publish-test-pypi.yml.github/workflows/publish-pypi.yml.github/workflows/publish-mcp-plugin.ymlanalyze_observableis_analysis_completeget_analysis_resultsget_enginesget_web_urlHere are practical prompt examples you can use with any MCP-capable assistant connected to Cyberbro.
MIT
Local cyber security assistant for PC issue detection, malware analysis, and system scanning
Share bugs, ideas, or general feedback.
MalChela malware analysis toolkit — exposes file analysis, string extraction, hash lookup, NSRL queries, and directory scanning to Claude via MCP. Built for DFIR analysts and malware researchers.
Core LimaCharlie skills for CLI-based API access, detection engineering, sensor tasking, case investigation, and fleet health monitoring.
Agentic SOC Platform integration for Claude Code
Binary reverse engineering, malware analysis, firmware security, and software protection research for authorized security research, CTF competitions, and defensive security
Claude plugins for SentinelOne XDR - threat detection, incident response, and endpoint agent management via the Purple AI MCP server