OpenVAS-MCP
Self-hosted MCP server that gives AI agents structured access to OpenVAS vulnerability scanning without sending data externally.
README
OpenVAS-MCP
A self-hosted MCP server that gives AI agents structured access to OpenVAS / Greenbone vulnerability scanning — without sending your data anywhere.
OpenVAS has no native interface for AI agents. Most integrations require cloud connectivity or expose GVM credentials to every client. OpenVAS-MCP solves this:
- Local-first. Talks only to your GVM instance. No telemetry, no external calls — verified by CI.
- Credential isolation. AI agents authenticate to the MCP server; the server holds the single GVM service account.
- Thin bridge. Returns structured scan data as-is. Analysis and reporting logic belong in the agent or a platform built on top.
See docs/architecture.md for a full architecture diagram and design details.
Quick start
0. Vibeinstall (optional, if you trust claude more than yourself)
Run in your terminal:
claude "install this, make no mistake."
If you prefer to stay in control, follow the manual setup below.
1. Get a GVM instance
Don't have one? Spin up the bundled Greenbone Community Edition stack:
docker compose -f docker/openvas/compose.yaml up -d
2. Connect an MCP client
stdio (Claude Desktop, Cursor, Windsurf, Cline, …)
Requirements: Python 3.10+
git clone https://github.com/CyberSecAuto-Labs/OpenVAS-MCP
cd OpenVAS-MCP
python3.11 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
Add to mcpServers in your client config file:
{
"mcpServers": {
"openvas": {
"command": "/path/to/.venv/bin/python", // ← edit this to your venv path
"args": ["-m", "openvas_mcp"],
"env": { "GVM_PASSWORD": "secret" } // ← edit this to your GVM password
}
}
}
Config file locations:
| Client | Path |
|---|---|
| Claude Desktop (macOS) | ~/Library/Application Support/Claude/claude_desktop_config.json |
| Claude Desktop (Windows) | %APPDATA%\Claude\claude_desktop_config.json |
| Cursor | ~/.cursor/mcp.json |
| Windsurf | ~/.codeium/windsurf/mcp_config.json |
| Cline / Roo Code | via the MCP panel in the VS Code extension |
HTTP/SSE (networked agents)
Requirements: Docker
Download the compose files from the latest release and run:
# GVM running locally via Unix socket
MCP_API_KEYS="supersecrettoken:my-agent" GVM_PASSWORD=secret docker compose up
# GVM on a remote host via TCP
MCP_API_KEYS="supersecrettoken:my-agent" GVM_HOST=192.168.1.10 GVM_PASSWORD=secret docker compose up
[!NOTE]
MCP_API_KEYSis a comma-separated list oftoken:namepairs sent as a Bearer token by the MCP client. Multiple clients:"tok1:agent1,tok2:agent2". PassMCP_ALLOW_UNAUTHENTICATED=1instead to skip auth on a trusted network.
Point your MCP client at the server:
{
"mcpServers": {
"openvas": {
"url": "http://your-server:8000/sse", // ← edit this to your server address
"headers": {
"Authorization": "Bearer supersecrettoken" // ← your MCP_API_KEYS token
}
}
}
}
[!WARNING] Plain TCP connections (
GVM_HOSTset,GVM_TLSunset) send GVM credentials unencrypted. UseGVM_TLS=1or a Unix socket for anything beyond local dev.
All-in-one dev setup
Greenbone Community Edition + MCP server from source in one go:
# Start the Greenbone stack
docker compose -f docker/openvas/compose.yaml up -d
# Start the MCP server, connected via gvmd socket
GVM_PASSWORD=secret docker compose -f compose.yaml -f compose.override.yaml up --build
[!TIP] See
compose.override.yamlfor how the socket volume is mounted.
Configuration
| Variable | Default | Description |
|---|---|---|
GVM_PASSWORD |
— | GVM password (required) |
GVM_SOCKET_PATH |
/run/gvmd/gvmd.sock |
Unix socket path (default connection) |
GVM_HOST |
— | Connect via TCP instead of socket (IPv4 and IPv6) |
MCP_TRANSPORT |
stdio |
stdio, sse, or streamable-http |
MCP_API_KEYS |
— | Bearer API keys for HTTP transport auth (token:name,...) |
See docs/configuration.md for the full reference, including TLS options, policy file, scan limits, and logging.
Available tools
| Tool | Description |
|---|---|
list_targets |
Return all scan targets |
create_target |
Create a target with specified hosts/CIDRs |
list_tasks |
Return all scan tasks |
start_scan |
Create and start a scan against a target |
get_scan_status |
Poll status and progress of a running scan |
fetch_scan_results |
Retrieve findings, optionally filtered by minimum severity |
Example: "Scan 192.168.1.0/24 and show me anything above severity 7" — the agent calls create_target → start_scan → get_scan_status → fetch_scan_results(min_severity=7.0).
Release integrity
Every release image is:
- Signed with cosign keyless OIDC signing — no long-lived key to compromise.
- SBOM attached — a CycloneDX JSON bill of materials is generated with syft and attached to each GitHub Release.
- Vulnerability-scanned — grype scans the SBOM on every PR (
vuln-scan.yml) and at release time, failing on fixablehighseverity findings (--only-fixed). - Egress-audited — the
startup-egressworkflow tracesconnect()syscalls viastraceon every push and PR, asserting no unexpected outbound connections at startup. Integration tests extend this to live GMP code paths.
Verify the image signature before running:
cosign verify \
--certificate-identity-regexp "https://github.com/CyberSecAuto-Labs/OpenVAS-MCP/.github/workflows/release.yml@refs/tags/.*" \
--certificate-oidc-issuer "https://token.actions.githubusercontent.com" \
ghcr.io/cybersecauto-labs/openvas-mcp:<version>
Docs
- docs/architecture.md — architecture diagram, component overview, and transport details
- docs/configuration.md — full environment variable reference, TLS, policy file, scan limits, logging
- docs/design.md — design decisions and known limitations
- docs/ci.md — CI workflows, guarantees, and tradeoffs
License
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.