37a867c485
- Complete Cloudflare Terraform configuration (DNS, WAF, tunnels, access) - WAF Intelligence MCP server with threat analysis and ML classification - GitOps automation with PR workflows and drift detection - Observatory monitoring stack with Prometheus/Grafana - IDE operator rules for governed development - Security playbooks and compliance frameworks - Autonomous remediation and state reconciliation
87 lines
2.1 KiB
Python
Executable File
87 lines
2.1 KiB
Python
Executable File
#!/usr/bin/env python3
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from __future__ import annotations
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import glob
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from dataclasses import asdict
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from typing import Any, Dict, List
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from modelcontextprotocol.python import Server
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from mcp.waf_intelligence.orchestrator import WAFInsight, WAFIntelligence
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server = Server("waf_intel")
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def _insight_to_dict(insight: WAFInsight) -> Dict[str, Any]:
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"""Convert a WAFInsight dataclass into a plain dict."""
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return asdict(insight)
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@server.tool()
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async def analyze_waf(
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file: str | None = None,
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files: List[str] | None = None,
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limit: int = 3,
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severity_threshold: str = "warning",
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) -> Dict[str, Any]:
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"""
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Analyze one or more Terraform WAF files and return curated insights.
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Args:
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file: Single file path (e.g. "terraform/waf.tf").
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files: Optional list of file paths or glob patterns (e.g. ["terraform/waf*.tf"]).
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limit: Max number of high-priority insights to return.
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severity_threshold: Minimum severity to include ("info", "warning", "error").
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Returns:
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{
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"results": [
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{
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"file": "...",
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"insights": [ ... ]
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},
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...
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]
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}
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"""
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paths: List[str] = []
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if files:
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for pattern in files:
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for matched in glob.glob(pattern):
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paths.append(matched)
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if file:
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paths.append(file)
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seen = set()
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unique_paths: List[str] = []
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for p in paths:
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if p not in seen:
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seen.add(p)
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unique_paths.append(p)
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if not unique_paths:
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raise ValueError("Please provide 'file' or 'files' to analyze.")
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intel = WAFIntelligence()
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results: List[Dict[str, Any]] = []
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for path in unique_paths:
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insights: List[WAFInsight] = intel.analyze_and_recommend(
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path,
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limit=limit,
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min_severity=severity_threshold,
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)
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results.append(
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{
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"file": path,
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"insights": [_insight_to_dict(insight) for insight in insights],
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}
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)
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return {"results": results}
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if __name__ == "__main__":
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server.run()
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