Initial commit: Cloudflare infrastructure with WAF Intelligence

- 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

waf_intel_mcp.py

@@ -0,0 +1,86 @@
+#!/usr/bin/env python3
+from __future__ import annotations
+
+import glob
+from dataclasses import asdict
+from typing import Any, Dict, List
+
+from modelcontextprotocol.python import Server
+from mcp.waf_intelligence.orchestrator import WAFInsight, WAFIntelligence
+
+server = Server("waf_intel")
+
+
+def _insight_to_dict(insight: WAFInsight) -> Dict[str, Any]:
+    """Convert a WAFInsight dataclass into a plain dict."""
+    return asdict(insight)
+
+
+@server.tool()
+async def analyze_waf(
+    file: str | None = None,
+    files: List[str] | None = None,
+    limit: int = 3,
+    severity_threshold: str = "warning",
+) -> Dict[str, Any]:
+    """
+    Analyze one or more Terraform WAF files and return curated insights.
+
+    Args:
+      file: Single file path (e.g. "terraform/waf.tf").
+      files: Optional list of file paths or glob patterns (e.g. ["terraform/waf*.tf"]).
+      limit: Max number of high-priority insights to return.
+      severity_threshold: Minimum severity to include ("info", "warning", "error").
+
+    Returns:
+      {
+        "results": [
+          {
+            "file": "...",
+            "insights": [ ... ]
+          },
+          ...
+        ]
+      }
+    """
+    paths: List[str] = []
+
+    if files:
+        for pattern in files:
+            for matched in glob.glob(pattern):
+                paths.append(matched)
+
+    if file:
+        paths.append(file)
+
+    seen = set()
+    unique_paths: List[str] = []
+    for p in paths:
+        if p not in seen:
+            seen.add(p)
+            unique_paths.append(p)
+
+    if not unique_paths:
+        raise ValueError("Please provide 'file' or 'files' to analyze.")
+
+    intel = WAFIntelligence()
+    results: List[Dict[str, Any]] = []
+
+    for path in unique_paths:
+        insights: List[WAFInsight] = intel.analyze_and_recommend(
+            path,
+            limit=limit,
+            min_severity=severity_threshold,
+        )
+        results.append(
+            {
+                "file": path,
+                "insights": [_insight_to_dict(insight) for insight in insights],
+            }
+        )
+
+    return {"results": results}
+
+
+if __name__ == "__main__":
+    server.run()