firewall-aiops
Governed OPNsense + pfSense firewall operations — gateway-health, rule-shadow, and blocked-traffic RCA, with guarded rule/alias writes, unbypassable audit logging (MCP + CLI), budget/runaway guards, dry-run, and undo/rollback.
README
<!-- mcp-name: io.github.AIops-tools/firewall-aiops -->
Firewall AIops
Governed, audited AI-ops for OPNsense and pfSense firewalls — for AI agents (via MCP) and humans (via CLI).
Disclaimer: Community-maintained open-source project. Not affiliated with, endorsed by, or sponsored by the OPNsense project, Deciso, Netgate, or the pfSense project. OPNsense, pfSense and Netgate are trademarks of their respective owners. MIT licensed.
firewall-aiops speaks to two firewall platforms behind one MCP server — OPNsense
(REST API under /api/..., API key+secret via HTTP Basic auth) and pfSense
(REST API v2 under /api/v2/... from the pfSense-pkg-RESTAPI package, API key via
an X-API-Key header) — with the same tools working on both. Each target in the
config names its own platform; a name-keyed platform registry selects the API shape
(auth + resource paths), so an agent never has to know which firewall it is talking to.
Every tool runs through a built-in governance harness (vendored, zero external dependency): audit log, token/call budget with runaway circuit-breaker, descriptive risk-tier labelling, undo-token recording, and prompt-injection sanitisation.
Why this exists
- One server, both firewalls — OPNsense and pfSense in a mixed estate, spoken to through identical tool names. Adding a third firewall later is a new platform descriptor, not a rewrite.
- Read the whole firewall — firmware/health, interfaces & gateways, filter rules (with hit counts and state table), NAT (port-forward / outbound / 1:1), aliases, VPN (WireGuard / OpenVPN / IPsec), DHCP leases & reservations, and the firewall log.
- Flagship RCA analyses — transparent heuristics that show their numbers, never a
black-box verdict:
gateway_health_rca(WAN loss/latency/down → cause + action),rule_hit_and_shadow_analysis(never-hit + shadowed/redundant rules), andblocked_traffic_rca(top blocked sources/ports → scan / brute-force / probe). - Governed writes — toggle a rule, add/remove an alias entry (reversible,
undo-recorded from the fetched before-state), flush states, restart a service, and
the "make it live" commit (
apply_changes/reconfigure) andrebootat risk=high with a dry-run preview. Every write, reversible or not, lands an audit row.
What this tool does, and does not, decide
It delivers firewall operations — reads and writes — accurately and efficiently, and records every one of them. It does not decide whether a write is allowed to happen. That is the agent's judgement, or the permission of the account you connect it with: give the OPNsense/pfSense API user a read-only role, and the writes fail at the server — the place that actually owns the permission.
So there is no read-only switch, no policy file, no approval gate to configure.
The one thing the tool guarantees is that nothing is silent: every call, over
MCP and over the CLI alike, lands an audit row in ~/.firewall-aiops/audit.db,
and reversible writes still capture their before-state and record an inverse
where one exists.
Each tool declares a
risk_level, matched to its[READ]/[WRITE]documentation tag, and carried into the audit row as a descriptive tier — so a reviewer can see at a glance that a row was a high-risk write. It is a label, not a gate.
Running a smaller / local model? See agent-guardrails.md — it lists the guardrails this tool now enforces for you (so you don't spend prompt budget restating them) and gives a ready-made system prompt for what's left.
Tool inventory (35 tools)
| Domain | Tools | # | Kind |
|---|---|---|---|
| System | firmware_status, health_status, interface_status, gateway_status |
4 | read |
| Rules | list_rules, rule_detail, rule_stats, rule_states |
4 | read |
| NAT | nat_port_forwards, nat_outbound, nat_one_to_one |
3 | read |
| Aliases | list_aliases, alias_entries |
2 | read |
| VPN | wireguard_status, openvpn_sessions, ipsec_sas |
3 | read |
| DHCP | dhcp_leases, dhcp_static_mappings |
2 | read |
| Diagnostics | firewall_log, states_table, top_talkers |
3 | read |
| Flagship analyses | gateway_health_rca, rule_hit_and_shadow_analysis, blocked_traffic_rca |
3 | read |
| Writes | toggle_rule, add_alias_entry, remove_alias_entry, kill_states, restart_service |
5 | write (med) |
| Writes | apply_changes, reconfigure, reboot |
3 | write (high) |
| Undo | undo_list, undo_apply |
2 | read / write |
Reversible writes record an inverse undo descriptor built from the real fetched
before-state (toggle_rule restores the rule's prior enabled flag; alias add/remove
invert). apply_changes / reconfigure / reboot are high-risk with dry_run;
reboot is irreversible (audit only).
Install
uv tool install firewall-aiops # or: pipx install firewall-aiops
Quick start
firewall-aiops init # wizard: pick platform (opnsense/pfsense) + store the secret (encrypted)
firewall-aiops doctor # verify config, secrets, and connectivity
firewall-aiops overview # one-shot: version + gateway/interface health + rule count
firewall-aiops rules list # list filter rules
firewall-aiops rules toggle <uuid> --disable # dry-run + double-confirm governed write
firewall-aiops log --action block -n 50 # recent blocked traffic
Run the MCP server (stdio) for an agent:
firewall-aiops mcp # or: firewall-aiops-mcp
MCP client config
{
"mcpServers": {
"firewall-aiops": {
"command": "uvx",
"args": ["--from", "firewall-aiops", "firewall-aiops-mcp"],
"env": { "FIREWALL_AIOPS_MASTER_PASSWORD": "your-master-password" }
}
}
}
Configuration
~/.firewall-aiops/config.yaml (non-secret connection details only):
targets:
- name: fw1
platform: opnsense # opnsense | pfsense
host: 192.0.2.1
port: 443
username: <opnsense-api-key> # OPNsense API key (unused for pfSense)
verify_ssl: false # false for self-signed lab certs
- name: edge
platform: pfsense
host: 192.0.2.2
verify_ssl: false
scheme: http # https (default) | http — for a GUI behind a TLS-terminating proxy
The secret — the OPNsense API secret (paired with the key for HTTP Basic auth)
or the pfSense API key — is stored encrypted in ~/.firewall-aiops/secrets.enc
(Fernet + scrypt-derived key), never plaintext on disk. Set it with
firewall-aiops secret set <target> or the init wizard. The store is unlocked by a
master password from FIREWALL_AIOPS_MASTER_PASSWORD (non-interactive/MCP/CI) or an
interactive prompt (CLI on a TTY). A legacy plaintext env var
FIREWALL_<TARGET>_SECRET is honoured as a fallback (migrate with
firewall-aiops secret migrate).
Governance
Every MCP tool is wrapped by @governed_tool:
- Audit — every call is logged to
~/.firewall-aiops/audit.db(tool, params with secrets redacted, status, duration, risk tier, approver, rationale). - Budget / runaway guard — per-process token/call caps and a repeat-call circuit
breaker (
FIREWALL_MAX_TOOL_CALLS,FIREWALL_RUNAWAY_MAX, …). - Risk-tier labelling — each tool's declared
risk_levelis recorded on its audit row as a descriptive tier (a label, not a gate); there is no read-only switch, policy file, or approval gate.FIREWALL_AUDIT_APPROVED_BYandFIREWALL_AUDIT_RATIONALEare optional audit annotations, recorded when set but never required. - Undo recording — reversible writes record an inverse descriptor to
~/.firewall-aiops/undo.dbfrom the fetched before-state (recording only; an external orchestrator executes it). - Sanitisation — all firewall-returned text is bounded + injection-sanitised before it reaches the agent.
Platform support & verification status
- Platforms: OPNsense (REST API) and pfSense (REST API v2, pfSense-pkg-RESTAPI).
- Test coverage: behaviour is validated against mocked OPNsense/pfSense JSON
responses — every module imports, every MCP tool carries the governance marker, the
RCA heuristics are unit-tested against synthetic telemetry, and reversible writes are
asserted to record the correct inverse undo descriptor. The concrete REST paths are
modelled from each project's public API and have not yet been exercised against a
live firewall. See docs/VERIFICATION.md for the checklist a
live run must satisfy;
firewall-aiops doctor(a firmware/version query on both platforms) is the fastest connectivity check. Both platforms are free and self-hostable (OPNsense is fully open-source; pfSense CE is free), so a home lab is the easiest place to run it. - Missing a capability? Open an issue or PR at github.com/AIops-tools/Firewall-AIops — contributions and feedback welcome.
License
MIT — see 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.
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.
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.
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.