pfsense-mcp-server
Reduces firewall management friction and human error by enabling natural-language configuration and compliance audits, lowering operational overhead and secu…
pfSense MCP Server enables security administrators to manage their pfSense firewalls using natural language through AI assistants like Claude Desktop. Simply ask "Show me blocked IPs" or "Run a PCI compliance check" instead of navigating complex interfaces. Supports REST/XML-RPC/SSH connections, and includes built-in compliance and guardrail
- Ask Claude to show all currently blocked IP addresses and their block reasons
- Generate a PCI DSS compliance report from your pfSense firewall configuration
- Automate security rule reviews by asking Claude to identify overly permissive access policies
Reduces firewall management friction and human error by enabling natural-language configuration and compliance audits, lowering operational overhead and security incident response time.
Security administrators managing pfSense deployments who need faster compliance verification and policy adjustments without manual console navigation.
https://github.com/gensecaihq/pfsense-mcp-server
By gensecaihq
How to Get It
claude mcp add pfsense-mcp-server -- npx -y pfsense-mcp-server
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
Once it’s connected, paste this into Claude:
Show all currently blocked IP addresses and their block reasons
Trust Signals Auto-scanned
Data & Access
Community Pulse New
No community discussions found yet. This doesn't mean the tool isn't good — it may be new or serve a niche use case.
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 83 GitHub stars; contributors unknown; last commit 0d ago; license MIT.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
How to evaluate tools before deploying →
Data shown here comes from public APIs and automated scanning. Reviewer notes reflect one person's experience. This is not a security certification or legal recommendation. Always evaluate tools according to your own organization's policies.
Evaluation
Scored from trust signals (evidence-eval-v1): 83 GitHub stars; contributors unknown; last commit 0d ago; license MIT.