fwrule-analyzer

Firewall Rule Analyzer

MCP Server

• Repository: AutomateIP/fwrule-mcp
• Transport: stdio (Python via uv run fwrule-mcp)
• Install: git clone + uv sync (or pip install -e .)
• Requires: No environment variables — standalone analysis engine
• Dependencies: fastmcp>=2.0, pydantic>=2.0, Python 3.11+

Available Tools (3)

Tool	What It Does
analyze_firewall_rule_overlap	Compare a candidate rule against an existing ruleset using 6-dimensional set intersection (zones, addresses, ports, protocols, actions, applications). Detects overlaps, shadowing, conflicts, and duplicates. Supports two input modes: vendor-native config via built-in parsers, or pre-normalized JSON.
parse_policy	Convert vendor-native firewall configurations into a standardized JSON schema. Enables inspection of parser output — rule counts, object resolution, address expansion — before running overlap analysis.
list_supported_vendors	Enumerate all supported firewall vendors, their aliases, configuration formats, and explain how to use normalized JSON input to bypass vendor-specific parsers.

Supported Vendors (10)

Vendor	Config Format	Versions	Identifiers
Palo Alto PAN-OS	XML export	9.x–11.x	panos, paloalto, panorama
Cisco ASA	show running-config	9.x+	asa, cisco-asa
Cisco FTD	FMC JSON	6.x–7.x	ftd, firepower, fmc
Cisco IOS/IOS-XE	show running-config	12.x–17.x	ios, iosxe, cisco-ios
Cisco IOS-XR	show running-config	6.x+	iosxr, ios-xr, xr
Check Point	JSON rulebase	R80.x–R82.x	checkpoint, cp, check-point
Juniper SRX	display set	19.x+	juniper, srx
Juniper Junos	display set	18.x+	junos, mx, ptx, qfx
Nokia SR OS	MD-CLI format	20.x+	sros, nokia, sr-os
Fortinet FortiOS	Full backup config	5.x–7.x	fortios, fortigate, fortinet, forti, fgt, fmg

⚠️ FortiOS parser contributed by SIA/NetClaw (Airowire Networks). PR open at AutomateIP/fwrule-mcp#1. Install from the fork until merged upstream: pip install git+https://github.com/akshaysiddaram/fwrule-mcp.git

Key Concepts

Concept	What It Means
Overlap	Candidate rule matches traffic already handled by existing rules
Shadowing	Candidate rule is fully covered by a higher-priority existing rule — it will never match
Conflict	Rules match the same traffic but have different actions (allow vs deny)
Duplication	Candidate rule is functionally identical to an existing rule
6-Dimensional Analysis	Comparison across source/dest zones, source/dest addresses, services/ports, protocols, actions, and applications
Normalized JSON	Vendor-agnostic rule schema with standardized fields (id, position, enabled, action, zones, addresses, services, applications)

Two Input Modes

Mode 1: Vendor-Native Config

Pass raw vendor configuration text and let the built-in parsers normalize it:

• vendor: Vendor identifier (e.g., panos, asa, ftd, ios, checkpoint, srx, junos, nokia, fortios)
• ruleset_payload: Complete firewall config in vendor format
• candidate_rule_payload: Single rule in vendor format
• os_version: Optional version hint for parser selection
• context_objects: Supplemental object definitions as JSON

Mode 2: Pre-Normalized JSON

Bypass parsers when structured data is already available:

• existing_rules: JSON array of normalized rule objects
• candidate_rule: Single normalized JSON rule object

Workflow: Pre-Change Rule Validation

When adding a new firewall rule to a policy:

1. Parse existing policy: parse_policy with vendor config — normalize to JSON, verify rule count and object resolution
2. Analyze candidate: analyze_firewall_rule_overlap — check the proposed rule against the existing ruleset
3. Review findings: Examine overlap type (shadow, conflict, duplicate, partial overlap), severity, and affected dimensions
4. Decision: Approve, modify, or reject the candidate rule based on findings
5. Report: Formatted analysis with vendor, policy, candidate rule, and overlap results

Workflow: Cross-Vendor Policy Audit

When auditing firewall rules across multiple platforms:

1. Enumerate vendors: list_supported_vendors — confirm supported platforms
2. Parse each policy: parse_policy for each vendor config — normalize all rules
3. Cross-analyze: For each rule in vendor A, use analyze_firewall_rule_overlap against vendor B's normalized rules
4. Identify: Cross-platform conflicts, redundant rules, shadowed entries
5. Report: Multi-vendor rule consistency analysis

Workflow: Ruleset Hygiene Audit

When cleaning up an existing firewall policy:

1. Parse policy: parse_policy — normalize the full ruleset
2. For each rule: analyze_firewall_rule_overlap — check the rule against all others in the same policy
3. Flag: Shadowed rules (dead rules that never match), duplicates, and internal conflicts
4. Report: Cleanup recommendations with rule positions and overlap details

Integration with Other Skills

Skill	How They Work Together
fmc-firewall-ops	FMC policy search + fwrule overlap analysis on retrieved rules
paloalto-panorama	Panorama policy export + fwrule cross-policy analysis
pyats-security	Device ACL retrieval via pyATS + fwrule overlap detection
pyats-asa-firewall	ASA config retrieval + fwrule ASA parser for rule normalization
fortimanager-ops	FortiManager policy export + fwrule FortiOS parser for cross-VDOM analysis
servicenow-change-workflow	ServiceNow CR gating + fwrule validation before rule deployment
github-ops	Commit firewall rule change analysis results to Git
gait-session-tracking	Audit trail for all firewall rule analysis operations

Important Rules

• Read-only analysis — all 3 tools are pure analysis; no firewall modifications
• No credentials required — works entirely on config text/JSON input, no API connections to firewalls
• Vendor parsers are best-effort — use Mode 2 (normalized JSON) when parsers produce unexpected results
• Record in GAIT — log all firewall rule analysis for compliance audit trail