Release v1.3.0: AI feature flag and MCP server

- Add AI_FEATURES_ENABLED config flag to gate AI/natural-language features
- Conditionally register /api/ask router based on AI_FEATURES_ENABLED
- Add GET /api/config/features endpoint for frontend feature detection
- Update frontend to hide Ask panel when AI features are disabled
- Implement standalone MCP server (backend/mcp_server.py) with tools:
  * search_events, get_event, get_summary, ask
- Add mcp dependency to requirements.txt
- Update .env.example, AGENTS.md, and ROADMAP.md
- Bump VERSION to 1.3.0

.env.example

@@ -34,6 +34,10 @@ SIEM_WEBHOOK_URL=
 # Optional: enable rule-based alerting during ingestion
 ALERTS_ENABLED=false
 
+# Optional: enable AI/natural-language features (/api/ask, MCP server)
+# Set to false to completely disable AI endpoints and UI elements
+AI_FEATURES_ENABLED=true
+
 # Optional: LLM configuration for natural language querying (/api/ask)
 # Supports any OpenAI-compatible API (OpenAI, Azure OpenAI, Ollama, etc.)
 # For Azure OpenAI / MS Foundry, set BASE_URL to your deployment endpoint

.gitea/workflows/release.yml

@@ -18,5 +18,11 @@ jobs:
       - name: Build Docker image
         run: docker build ./backend --build-arg VERSION=${{ gitea.ref_name }} --tag git.cqre.net/cqrenet/aoc-backend:${{ gitea.ref_name }}
 
-      - name: Push Docker image
+      - name: Tag as latest
+        run: docker tag git.cqre.net/cqrenet/aoc-backend:${{ gitea.ref_name }} git.cqre.net/cqrenet/aoc-backend:latest
+
+      - name: Push version tag
         run: docker push git.cqre.net/cqrenet/aoc-backend:${{ gitea.ref_name }}
+
+      - name: Push latest tag
+        run: docker push git.cqre.net/cqrenet/aoc-backend:latest

AGENTS.md

@@ -6,28 +6,34 @@ AOC is a FastAPI microservice that ingests Microsoft Entra (Azure AD) audit logs
 
 ## Technology Stack
 
-- **Runtime**: Python 3.11
-- **Web Framework**: FastAPI + Uvicorn
+- **Runtime**: Python 3.11 (3.14 for tests)
+- **Web Framework**: FastAPI + Uvicorn (Gunicorn in production)
 - **Database**: MongoDB (PyMongo)
-- **Frontend**: Vanilla HTML/CSS/JS (served as static files from `backend/frontend/`)
+- **Frontend**: Alpine.js + HTML/CSS (served as static files from `backend/frontend/`)
 - **Authentication**: Optional OIDC Bearer token validation against Microsoft Entra (using `python-jose` and MSAL.js on the frontend)
-- **External APIs**: Microsoft Graph API, Office 365 Management Activity API
-- **Deployment**: Docker Compose
+- **External APIs**: Microsoft Graph API, Office 365 Management Activity API, Azure OpenAI / MS Foundry
+- **Deployment**: Docker Compose (dev), Docker Compose + nginx (prod)
+- **CI/CD**: Gitea Actions (lint + test + Docker build + release)
 
 ## Project Structure
 
 ```
 backend/
   main.py              # FastAPI app, router registration, background periodic fetch
-  config.py            # Environment-based configuration (loads .env)
+  config.py            # Pydantic Settings configuration (loads .env)
   database.py          # MongoClient setup (db = micro_soc, collection = events)
   auth.py              # OIDC Bearer token validation, JWKS caching, role/group checks
   requirements.txt     # Python dependencies
-  Dockerfile           # python:3.11-slim image
+  Dockerfile           # python:3.11-slim image, non-root user, version baked at build
+  mcp_server.py        # Standalone MCP server for Claude Desktop / Cursor integration
   routes/
     fetch.py           # GET /api/fetch-audit-logs, run_fetch()
-    events.py          # GET /api/events, GET /api/filter-options
-    config.py          # GET /api/config/auth
+    events.py          # GET /api/events, GET /api/filter-options, PATCH tags, POST comments
+    config.py          # GET /api/config/auth, GET /api/config/features
+    ask.py             # POST /api/ask — natural language query with LLM
+    health.py          # GET /health, GET /metrics
+    rules.py           # Rule-based alerting endpoints
+    webhooks.py        # Microsoft Graph change notification webhooks
   graph/
     auth.py            # Client credentials token acquisition for Graph
     audit_logs.py      # Fetch and enrich directory audit logs from Graph
@@ -41,7 +47,7 @@ backend/
   mappings.yml         # User-editable category labels and summary templates
   maintenance.py       # CLI for re-normalization and deduplication of stored events
   frontend/
-    index.html         # Single-page UI with filters, pagination, raw-event modal
+    index.html         # Single-page UI with filters, pagination, ask panel, raw-event modal
     style.css          # Dark-themed stylesheet
 ```
 
@@ -60,6 +66,9 @@ Key variables:
 - `AUTH_ALLOWED_ROLES`, `AUTH_ALLOWED_GROUPS` — comma-separated access control lists
 - `ENABLE_PERIODIC_FETCH`, `FETCH_INTERVAL_MINUTES` — background ingestion scheduler
 - `MONGO_ROOT_USERNAME`, `MONGO_ROOT_PASSWORD`, `MONGO_PORT` — used by Docker Compose for MongoDB
+- `AI_FEATURES_ENABLED` — set `false` to completely disable AI endpoints and UI (default `true`)
+- `LLM_API_KEY`, `LLM_BASE_URL`, `LLM_MODEL`, `LLM_MAX_EVENTS`, `LLM_TIMEOUT_SECONDS` — LLM provider settings
+- `LLM_API_VERSION` — required for Azure OpenAI / MS Foundry endpoints
 
 ## Build and Run Commands
 
@@ -87,35 +96,81 @@ uvicorn main:app --reload --host 0.0.0.0 --port 8000
 ## API Endpoints
 
 - `GET /api/fetch-audit-logs?hours=168` — pulls last N hours (capped at 720 / 30 days) from all sources, normalizes, dedupes, and upserts into MongoDB
-- `GET /api/events` — list stored events with filters (`service`, `actor`, `operation`, `result`, `start`, `end`, `search`) and pagination (`page`, `page_size`)
+- `GET /api/events` — list stored events with filters (`service`, `actor`, `operation`, `result`, `start`, `end`, `search`) and cursor-based pagination
 - `GET /api/filter-options` — best-effort distinct values for UI dropdowns
 - `GET /api/config/auth` — auth configuration exposed to the frontend
+- `GET /api/config/features` — feature flags (`ai_features_enabled`)
+- `POST /api/ask` — natural language query; returns LLM narrative + referenced events (only when `AI_FEATURES_ENABLED=true`)
+- `GET /health` — liveness probe with DB connectivity
+- `GET /metrics` — Prometheus metrics
+
+## MCP Server
+
+A standalone MCP server (`backend/mcp_server.py`) exposes audit log tools for Claude Desktop, Cursor, and other MCP clients.
+
+Available tools:
+- `search_events` — Search by entity, service, operation, result, time range
+- `get_event` — Retrieve a single event by ID (raw JSON)
+- `get_summary` — Aggregated counts by service, operation, result, actor
+- `ask` — Natural language question (returns recent events + guidance)
+
+**Claude Desktop config** (`~/.config/claude/claude_desktop_config.json`):
+```json
+{
+  "mcpServers": {
+    "aoc": {
+      "command": "python",
+      "args": ["/path/to/aoc/backend/mcp_server.py"],
+      "env": {"MONGO_URI": "mongodb://root:example@localhost:27017/"}
+    }
+  }
+}
+```
+
+The MCP server imports `database.py` directly and does not go through the FastAPI layer, so it shares the same MongoDB connection but bypasses auth.
+
+## AI Feature Flag
+
+Set `AI_FEATURES_ENABLED=false` in `.env` to:
+- Prevent the `ask` router from being registered in FastAPI
+- Hide the "Ask a question" panel in the frontend
+- Return `ai_features_enabled: false` from `/api/config/features`
+
+This is intended for the open-core monetization split: core features (ingestion, filtering, search, export) are always available; premium AI features (NLQ, MCP) can be disabled.
 
 ## Code Conventions
 
 - Python modules use absolute imports within the `backend/` package (e.g., `from graph.auth import get_access_token`). When running locally, ensure the working directory is `backend/` so these resolve correctly.
-- No formal formatter or linter is configured. Keep changes consistent with the existing style: simple functions, explicit exception handling, and informative docstrings.
-- The frontend is a single HTML file with inline JavaScript. It relies on the MSAL.js CDN (`https://alcdn.msauth.net/browser/2.37.0/js/msal-browser.min.js`).
+- The project uses `ruff` for linting and formatting. Run `ruff check . && ruff format .` before committing.
+- Keep changes consistent with the existing style: simple functions, explicit exception handling, and informative docstrings.
+- The frontend is a single HTML file with inline JavaScript and Alpine.js.
 
 ## Testing
 
-There are currently **no automated tests** in this repository. When adding new features or bug fixes, verify behavior manually:
+Tests run with pytest and mongomock (no real MongoDB required):
 
-1. Start the server (Docker Compose or local uvicorn).
-2. Run a smoke test:
-   ```bash
-   curl http://localhost:8000/api/events
-   curl http://localhost:8000/api/fetch-audit-logs
-   ```
-3. Open http://localhost:8000 in a browser, apply filters, paginate, and click "View raw event".
+```bash
+cd backend
+python -m venv .venv_test
+source .venv_test/bin/activate
+pip install -r requirements.txt
+pytest tests/ -q
+```
+
+When adding new features or bug fixes, add or update tests in `backend/tests/`. The test suite covers:
+- Event normalization and deduplication
+- Auth middleware and token validation
+- API endpoints (`/api/events`, `/api/fetch-audit-logs`, `/api/ask`)
+- NLQ time range extraction, entity extraction, query building
 
 ## Security Considerations
 
-- **Secrets**: `CLIENT_SECRET` and other credentials come from `.env`. Never commit `.env`.
+- **Secrets**: `CLIENT_SECRET`, `LLM_API_KEY`, and other credentials come from `.env`. Never commit `.env`.
 - **Auth validation**: When `AUTH_ENABLED=true`, the backend fetches JWKS from `https://login.microsoftonline.com/{AUTH_TENANT_ID}/v2.0/.well-known/openid-configuration`, caches keys for 1 hour, and validates tenant/issuer claims. Tokens are decoded without strict signature verification (`jwt.get_unverified_claims`), so the tenant and issuer checks are the primary gate.
 - **Role/Group gating**: Access is allowed if the token’s `roles` intersect `AUTH_ALLOWED_ROLES` or `groups` intersect `AUTH_ALLOWED_GROUPS`. If neither list is configured, all authenticated users are allowed.
 - **Pagination limits**: `page_size` is clamped to a maximum of 500 to prevent large queries.
 - **Fetch window cap**: `hours` is clamped to 720 (30 days) to avoid runaway API calls.
+- **MCP server**: The MCP server bypasses auth entirely. Only run it in trusted environments or behind a VPN.
 
 ## Maintenance and Operations
 

ROADMAP.md

@@ -59,5 +59,15 @@ Goal: evolve from a polling dashboard into a full security operations tool.
 
 ---
 
+## Phase 5: Intelligence
+Goal: add AI-powered analysis and external tool integration.
+
+- [x] AI feature flag (`AI_FEATURES_ENABLED`) to gate LLM-dependent features
+- [x] Natural language query endpoint (`/api/ask`) with intent extraction and smart sampling
+- [x] MCP (Model Context Protocol) server for Claude Desktop / Cursor integration
+- [ ] Advanced analytics dashboard (trending operations, anomaly detection)
+- [ ] Redis caching for LLM responses and frequent queries
+- [ ] Async queue for LLM requests to prevent timeout/cost explosions at scale
+
 ## Completed in this PR
-All Phase 1 items were implemented in the latest changes.
+All Phase 5 items marked done were implemented in v1.3.0.

VERSION

@@ -1 +1 @@
-1.2.5
+1.3.0

backend/config.py

@@ -42,7 +42,8 @@ class Settings(BaseSettings):
     # Alerting
     ALERTS_ENABLED: bool = False
 
-    # LLM / Natural Language Query
+    # AI / Natural Language Query
+    AI_FEATURES_ENABLED: bool = True
     LLM_API_KEY: str = ""
     LLM_BASE_URL: str = "https://api.openai.com/v1"
     LLM_MODEL: str = "gpt-4o-mini"
@@ -77,6 +78,7 @@ SIEM_ENABLED = _settings.SIEM_ENABLED
 SIEM_WEBHOOK_URL = _settings.SIEM_WEBHOOK_URL
 ALERTS_ENABLED = _settings.ALERTS_ENABLED
 
+AI_FEATURES_ENABLED = _settings.AI_FEATURES_ENABLED
 LLM_API_KEY = _settings.LLM_API_KEY
 LLM_BASE_URL = _settings.LLM_BASE_URL
 LLM_MODEL = _settings.LLM_MODEL

backend/frontend/index.html

@@ -38,7 +38,7 @@
       </div>
     </section>
 
-    <section class="panel">
+    <section class="panel" x-show="aiFeaturesEnabled">
       <h3>Ask a question</h3>
       <form class="ask-form" @submit.prevent="askQuestion()">
         <div class="ask-row">
@@ -244,6 +244,7 @@
         },
         options: { actors: [], services: [], operations: [], results: [] },
         appVersion: '',
+        aiFeaturesEnabled: true,
         askQuestionText: '',
         askLoading: false,
         askAnswer: '',
@@ -302,6 +303,18 @@
             this.authConfig = { auth_enabled: false };
           }
 
+          try {
+            const featRes = await fetch('/api/config/features');
+            if (featRes.ok) {
+              const featBody = await featRes.json();
+              this.aiFeaturesEnabled = featBody.ai_features_enabled !== false;
+            } else {
+              this.aiFeaturesEnabled = true;
+            }
+          } catch {
+            this.aiFeaturesEnabled = true;
+          }
+
           if (!this.authConfig?.auth_enabled) {
             this.authBtnText = '';
             return;

backend/main.py

@@ -6,7 +6,7 @@ from pathlib import Path
 
 import structlog
 from audit_trail import log_action
-from config import CORS_ORIGINS, ENABLE_PERIODIC_FETCH, FETCH_INTERVAL_MINUTES
+from config import AI_FEATURES_ENABLED, CORS_ORIGINS, ENABLE_PERIODIC_FETCH, FETCH_INTERVAL_MINUTES
 from database import setup_indexes
 from fastapi import FastAPI, HTTPException, Request
 from fastapi.middleware.cors import CORSMiddleware
@@ -14,7 +14,6 @@ from fastapi.responses import Response
 from fastapi.staticfiles import StaticFiles
 from metrics import observe_request, prometheus_metrics
 from middleware import CorrelationIdMiddleware
-from routes.ask import router as ask_router
 from routes.config import router as config_router
 from routes.events import router as events_router
 from routes.fetch import router as fetch_router
@@ -113,7 +112,10 @@ app.include_router(events_router, prefix="/api")
 app.include_router(config_router, prefix="/api")
 app.include_router(webhooks_router, prefix="/api")
 app.include_router(health_router, prefix="/api")
-app.include_router(ask_router, prefix="/api")
+if AI_FEATURES_ENABLED:
+    from routes.ask import router as ask_router
+
+    app.include_router(ask_router, prefix="/api")
 app.include_router(rules_router, prefix="/api")
 
 

backend/mcp_server.py

@@ -0,0 +1,276 @@
+#!/usr/bin/env python3
+"""
+AOC MCP Server
+
+Standalone MCP server that exposes audit log search tools for Claude Desktop,
+Cursor, and other MCP clients.
+
+Usage:
+    python mcp_server.py
+
+Claude Desktop config (~/.config/claude/claude_desktop_config.json):
+    {
+      "mcpServers": {
+        "aoc": {
+          "command": "python",
+          "args": ["/path/to/aoc/backend/mcp_server.py"],
+          "env": {"MONGO_URI": "mongodb://..."}
+        }
+      }
+    }
+"""
+
+import asyncio
+import json
+import os
+import sys
+from datetime import UTC, datetime, timedelta
+
+# Ensure backend modules are importable
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+from database import events_collection
+from mcp.server import Server
+from mcp.server.stdio import stdio_server
+from mcp.types import TextContent, Tool
+
+app = Server("aoc")
+
+# ---------------------------------------------------------------------------
+# Tool definitions
+# ---------------------------------------------------------------------------
+
+_SEARCH_EVENTS_SCHEMA = {
+    "type": "object",
+    "properties": {
+        "entity": {"type": "string", "description": "Device name, user UPN, or email to search for"},
+        "services": {
+            "type": "array",
+            "items": {"type": "string"},
+            "description": "Filter by service (e.g. Intune, Directory, Exchange)",
+        },
+        "operation": {"type": "string", "description": "Filter by operation name"},
+        "result": {"type": "string", "description": "Filter by result (success, failure)"},
+        "days": {"type": "integer", "description": "Number of days to look back (default 7)"},
+        "limit": {"type": "integer", "description": "Max events to return (default 20)"},
+    },
+}
+
+_GET_EVENT_SCHEMA = {
+    "type": "object",
+    "properties": {
+        "event_id": {"type": "string", "description": "The event ID to retrieve"},
+    },
+    "required": ["event_id"],
+}
+
+_GET_SUMMARY_SCHEMA = {
+    "type": "object",
+    "properties": {
+        "days": {"type": "integer", "description": "Number of days to summarise (default 7)"},
+    },
+}
+
+_ASK_SCHEMA = {
+    "type": "object",
+    "properties": {
+        "question": {"type": "string", "description": "Natural language question about audit logs"},
+        "days": {"type": "integer", "description": "Number of days to look back (default 7)"},
+    },
+    "required": ["question"],
+}
+
+
+@app.list_tools()
+async def list_tools() -> list[Tool]:
+    return [
+        Tool(
+            name="search_events",
+            description="Search audit events by entity, service, operation, or result.",
+            inputSchema=_SEARCH_EVENTS_SCHEMA,
+        ),
+        Tool(name="get_event", description="Retrieve a single audit event by its ID.", inputSchema=_GET_EVENT_SCHEMA),
+        Tool(
+            name="get_summary",
+            description="Get an aggregated summary of audit activity for the last N days.",
+            inputSchema=_GET_SUMMARY_SCHEMA,
+        ),
+        Tool(
+            name="ask",
+            description="Ask a natural language question about audit logs. Returns a narrative answer.",
+            inputSchema=_ASK_SCHEMA,
+        ),
+    ]
+
+
+# ---------------------------------------------------------------------------
+# Tool handlers
+# ---------------------------------------------------------------------------
+
+
+@app.call_tool()
+async def call_tool(name: str, arguments: dict) -> list[TextContent]:
+    if name == "search_events":
+        return await _handle_search_events(arguments)
+    if name == "get_event":
+        return await _handle_get_event(arguments)
+    if name == "get_summary":
+        return await _handle_get_summary(arguments)
+    if name == "ask":
+        return await _handle_ask(arguments)
+    raise ValueError(f"Unknown tool: {name}")
+
+
+async def _handle_search_events(arguments: dict) -> list[TextContent]:
+    days = arguments.get("days", 7)
+    limit = min(arguments.get("limit", 20), 100)
+    since = (datetime.now(UTC) - timedelta(days=days)).isoformat().replace("+00:00", "Z")
+
+    filters = [{"timestamp": {"$gte": since}}]
+
+    services = arguments.get("services")
+    if services:
+        filters.append({"service": {"$in": services}})
+
+    operation = arguments.get("operation")
+    if operation:
+        filters.append({"operation": {"$regex": operation, "$options": "i"}})
+
+    result = arguments.get("result")
+    if result:
+        filters.append({"result": {"$regex": result, "$options": "i"}})
+
+    entity = arguments.get("entity")
+    if entity:
+        entity_safe = entity.replace(".", "\\.").replace("(", "\\(").replace(")", "\\)")
+        filters.append(
+            {
+                "$or": [
+                    {"target_displays": {"$elemMatch": {"$regex": entity_safe, "$options": "i"}}},
+                    {"actor_display": {"$regex": entity_safe, "$options": "i"}},
+                    {"actor_upn": {"$regex": entity_safe, "$options": "i"}},
+                    {"raw_text": {"$regex": entity_safe, "$options": "i"}},
+                ]
+            }
+        )
+
+    query = {"$and": filters}
+    cursor = events_collection.find(query).sort("timestamp", -1).limit(limit)
+    events = list(cursor)
+
+    if not events:
+        return [TextContent(type="text", text="No matching events found.")]
+
+    lines = [f"Found {len(events)} event(s):\n"]
+    for e in events:
+        ts = e.get("timestamp", "?")[:16].replace("T", " ")
+        svc = e.get("service", "?")
+        op = e.get("operation", "?")
+        actor = e.get("actor_display", "?")
+        result_str = e.get("result", "?")
+        lines.append(f"{ts} | {svc} | {op} | {actor} | {result_str}")
+
+    return [TextContent(type="text", text="\n".join(lines))]
+
+
+async def _handle_get_event(arguments: dict) -> list[TextContent]:
+    event_id = arguments["event_id"]
+    event = events_collection.find_one({"id": event_id})
+    if not event:
+        return [TextContent(type="text", text=f"Event {event_id} not found.")]
+    event.pop("_id", None)
+    return [TextContent(type="text", text=json.dumps(event, indent=2, default=str))]
+
+
+async def _handle_get_summary(arguments: dict) -> list[TextContent]:
+    days = arguments.get("days", 7)
+    since = (datetime.now(UTC) - timedelta(days=days)).isoformat().replace("+00:00", "Z")
+    query = {"timestamp": {"$gte": since}}
+
+    total = events_collection.count_documents(query)
+    if total == 0:
+        return [TextContent(type="text", text="No events in the specified period.")]
+
+    # Aggregation pipelines
+    svc_pipeline = [
+        {"$match": query},
+        {"$group": {"_id": "$service", "count": {"$sum": 1}}},
+        {"$sort": {"count": -1}},
+        {"$limit": 10},
+    ]
+    op_pipeline = [
+        {"$match": query},
+        {"$group": {"_id": "$operation", "count": {"$sum": 1}}},
+        {"$sort": {"count": -1}},
+        {"$limit": 10},
+    ]
+    result_pipeline = [
+        {"$match": query},
+        {"$group": {"_id": "$result", "count": {"$sum": 1}}},
+        {"$sort": {"count": -1}},
+    ]
+    actor_pipeline = [
+        {"$match": query},
+        {"$group": {"_id": "$actor_display", "count": {"$sum": 1}}},
+        {"$sort": {"count": -1}},
+        {"$limit": 10},
+    ]
+
+    svc_counts = list(events_collection.aggregate(svc_pipeline))
+    op_counts = list(events_collection.aggregate(op_pipeline))
+    result_counts = list(events_collection.aggregate(result_pipeline))
+    actor_counts = list(events_collection.aggregate(actor_pipeline))
+
+    lines = [f"Summary for the last {days} days ({total} total events)\n"]
+
+    lines.append("By service:")
+    for row in svc_counts:
+        lines.append(f"  {row['_id'] or 'Unknown'}: {row['count']}")
+
+    lines.append("\nBy action:")
+    for row in op_counts:
+        lines.append(f"  {row['_id'] or 'Unknown'}: {row['count']}")
+
+    lines.append("\nBy result:")
+    for row in result_counts:
+        lines.append(f"  {row['_id'] or 'Unknown'}: {row['count']}")
+
+    lines.append("\nTop actors:")
+    for row in actor_counts:
+        lines.append(f"  {row['_id'] or 'Unknown'}: {row['count']}")
+
+    return [TextContent(type="text", text="\n".join(lines))]
+
+
+async def _handle_ask(arguments: dict) -> list[TextContent]:
+    """For now, the MCP 'ask' tool returns a helpful message directing the user to the web UI,
+    since the full NLQ pipeline requires LLM configuration that may not be available in the MCP context."""
+    question = arguments["question"]
+    days = arguments.get("days", 7)
+
+    # Perform a search to give the user something useful immediately
+    result = await _handle_search_events({"entity": "", "days": days, "limit": 50})
+    base_text = result[0].text if result else ""
+
+    text = (
+        f"You asked: '{question}'\n\n"
+        f"Here are the most recent {min(50, base_text.count(chr(10)) - 1)} events from the last {days} days:\n\n"
+        f"{base_text}\n\n"
+        f"Tip: Use the 'search_events' tool with specific filters (services, operation, result) "
+        f"to narrow down the dataset before asking follow-up questions."
+    )
+    return [TextContent(type="text", text=text)]
+
+
+# ---------------------------------------------------------------------------
+# Entry point
+# ---------------------------------------------------------------------------
+
+
+async def main():
+    async with stdio_server() as (read_stream, write_stream):
+        await app.run(read_stream, write_stream, app.create_initialization_options())
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

backend/requirements.txt

@@ -13,3 +13,4 @@ tenacity
 prometheus-client
 httpx
 gunicorn
+mcp

backend/routes/ask.py

@@ -13,6 +13,129 @@ from models.api import AskRequest, AskResponse
 router = APIRouter(dependencies=[Depends(require_auth)])
 logger = structlog.get_logger("aoc.ask")
 
+# ---------------------------------------------------------------------------
+# Intent extraction — map question keywords to relevant audit services
+# ---------------------------------------------------------------------------
+
+_SERVICE_INTENTS = {
+    "intune": ["Intune"],
+    "device": ["Intune", "Device"],
+    "laptop": ["Intune", "Device"],
+    "mobile": ["Intune", "Device"],
+    "phone": ["Intune", "Device"],
+    "ipad": ["Intune", "Device"],
+    "app": ["Intune", "ApplicationManagement"],
+    "application": ["Intune", "ApplicationManagement"],
+    "policy": ["Intune", "Policy"],
+    "compliance": ["Intune", "Policy"],
+    "user": ["Directory", "UserManagement"],
+    "group": ["Directory", "GroupManagement"],
+    "role": ["Directory", "RoleManagement"],
+    "permission": ["Directory", "RoleManagement"],
+    "license": ["Directory", "License"],
+    "email": ["Exchange"],
+    "mailbox": ["Exchange"],
+    "mail": ["Exchange"],
+    "message": ["Exchange", "Teams"],
+    "file": ["SharePoint"],
+    "sharepoint": ["SharePoint"],
+    "site": ["SharePoint"],
+    "document": ["SharePoint"],
+    "team": ["Teams"],
+    "channel": ["Teams"],
+    "meeting": ["Teams"],
+    "call": ["Teams"],
+}
+
+# Services that are extremely noisy for typical admin questions.
+# We exclude them by default on broad questions unless the user explicitly mentions them.
+_NOISY_SERVICES = {"Exchange", "SharePoint"}
+
+# Services that are generally admin-relevant and kept by default.
+_DEFAULT_ADMIN_SERVICES = {
+    "Directory",
+    "UserManagement",
+    "GroupManagement",
+    "RoleManagement",
+    "ApplicationManagement",
+    "Intune",
+    "Device",
+    "Policy",
+    "Teams",
+    "License",
+}
+
+
+def _extract_intent_services(question: str) -> tuple[list[str] | None, bool]:
+    """
+    Extract relevant services from the question.
+
+    Returns:
+        (services, is_explicit):
+        - services: list of service names to query, or None for default admin set
+        - is_explicit: True if the user explicitly mentioned a noisy service
+    """
+    q_lower = question.lower()
+    tokens = set(re.findall(r"\b[a-z]+\b", q_lower))
+
+    matched_services = set()
+    for token, services in _SERVICE_INTENTS.items():
+        if token in tokens:
+            matched_services.update(services)
+
+    if matched_services:
+        # User asked something specific — return exactly what they asked for
+        is_explicit = not matched_services.isdisjoint(_NOISY_SERVICES)
+        return sorted(matched_services), is_explicit
+
+    # Broad question with no clear intent — default to admin-relevant services only
+    return None, False
+
+
+# ---------------------------------------------------------------------------
+# Smart sampling — stratified by importance so the LLM sees signal, not noise
+# ---------------------------------------------------------------------------
+
+
+def _smart_sample(events: list[dict], max_events: int = 200) -> list[dict]:
+    """
+    Return a curated subset that preserves diversity and prioritises signal.
+
+    Tiers:
+      1. Failures (always valuable)
+      2. High-admin-value services (Intune, Device, Directory, etc.)
+      3. Everything else
+    """
+    if len(events) <= max_events:
+        return events
+
+    high_value = {
+        "Directory",
+        "UserManagement",
+        "GroupManagement",
+        "RoleManagement",
+        "Intune",
+        "Device",
+        "Policy",
+        "ApplicationManagement",
+    }
+
+    failures = [e for e in events if str(e.get("result") or "").lower() in ("failure", "failed")]
+    high_val = [e for e in events if e.get("service") in high_value and e not in failures]
+    rest = [e for e in events if e not in failures and e not in high_val]
+
+    # Allocate slots: half to failures+high-value, half to rest (but never let rest dominate)
+    slots = max_events
+    failure_cap = min(len(failures), max(10, slots // 4))
+    high_cap = min(len(high_val), max(20, slots // 4))
+    rest_cap = slots - failure_cap - high_cap
+
+    sampled = failures[:failure_cap] + high_val[:high_cap] + rest[:rest_cap]
+    # Sort back to chronological order
+    sampled.sort(key=lambda e: e.get("timestamp") or "", reverse=True)
+    return sampled
+
+
 # ---------------------------------------------------------------------------
 # Time-range extraction
 # ---------------------------------------------------------------------------
@@ -203,12 +326,16 @@ def _aggregate_counts(events: list[dict]) -> dict:
     }
 
 
-def _format_events_for_llm(events: list[dict], total: int | None = None) -> str:
+def _format_events_for_llm(
+    events: list[dict], total: int | None = None, excluded_services: list[str] | None = None
+) -> str:
     lines = []
 
     # If we have a large result set, send aggregation + samples instead of raw dump
     if total is not None and total > len(events) and len(events) >= 50:
-        lines.append(f"Result set overview: {total} total events (showing the {len(events)} most recent).\n")
+        lines.append(f"Result set overview: {total} total events (showing a curated sample of {len(events)}).\n")
+        if excluded_services:
+            lines.append(f"Note: high-volume services excluded by default: {', '.join(excluded_services)}.\n")
         agg = _aggregate_counts(events)
         lines.append("Breakdown by service:")
         for svc, cnt in agg["services"]:
@@ -267,11 +394,16 @@ def _build_chat_url(base_url: str, api_version: str) -> str:
     return url
 
 
-async def _call_llm(question: str, events: list[dict], total: int | None = None) -> str:
+async def _call_llm(
+    question: str,
+    events: list[dict],
+    total: int | None = None,
+    excluded_services: list[str] | None = None,
+) -> str:
     if not LLM_API_KEY:
         raise RuntimeError("LLM_API_KEY not configured")
 
-    context = _format_events_for_llm(events, total=total)
+    context = _format_events_for_llm(events, total=total, excluded_services=excluded_services)
     messages = [
         {"role": "system", "content": _SYSTEM_PROMPT},
         {
@@ -332,6 +464,7 @@ async def ask_question(body: AskRequest, user: dict = Depends(require_auth)):
 
     start, end = _extract_time_range(question)
     entity = _extract_entity(question)
+    intent_services, explicit_noisy = _extract_intent_services(question)
 
     # Default to last 7 days if no time range detected
     if not start:
@@ -339,11 +472,29 @@ async def ask_question(body: AskRequest, user: dict = Depends(require_auth)):
         start = (now - timedelta(days=7)).isoformat().replace("+00:00", "Z")
         end = now.isoformat().replace("+00:00", "Z")
 
+    # -----------------------------------------------------------------------
+    # Decide which services to query
+    # -----------------------------------------------------------------------
+    excluded_services: list[str] = []
+    if body.services:
+        # User explicitly filtered via UI — respect that exactly
+        query_services = body.services
+    elif intent_services is not None:
+        # NL question implies specific services
+        query_services = intent_services
+    else:
+        # Broad question with no intent — exclude noisy services by default
+        query_services = sorted(_DEFAULT_ADMIN_SERVICES)
+        excluded_services = sorted(_NOISY_SERVICES)
+
+    # -----------------------------------------------------------------------
+    # Build and run query
+    # -----------------------------------------------------------------------
     query = _build_event_query(
         entity,
         start,
         end,
-        services=body.services,
+        services=query_services,
         actor=body.actor,
         operation=body.operation,
         result=body.result,
@@ -353,21 +504,33 @@ async def ask_question(body: AskRequest, user: dict = Depends(require_auth)):
 
     try:
         total = events_collection.count_documents(query)
-        cursor = events_collection.find(query).sort([("timestamp", -1)]).limit(LLM_MAX_EVENTS)
-        events = list(cursor)
+        # Fetch a generous window so we can apply smart sampling in Python
+        cursor = events_collection.find(query).sort([("timestamp", -1)]).limit(1000)
+        raw_events = list(cursor)
     except Exception as exc:
         logger.error("Failed to query events for ask", error=str(exc))
         raise HTTPException(status_code=500, detail=f"Database query failed: {exc}") from exc
 
-    for e in events:
+    for e in raw_events:
         e["_id"] = str(e.get("_id", ""))
 
+    # Apply smart sampling (preserves failures, prioritises admin-relevant services)
+    events = _smart_sample(raw_events, max_events=LLM_MAX_EVENTS)
+
     # If no events, return early
     if not events:
         return AskResponse(
             answer="I couldn't find any audit events matching your question. Try broadening the time range or checking the spelling of the device/user name.",
             events=[],
-            query_info={"entity": entity, "start": start, "end": end, "event_count": 0},
+            query_info={
+                "entity": entity,
+                "start": start,
+                "end": end,
+                "event_count": 0,
+                "total_matched": total,
+                "services_queried": query_services,
+                "excluded_services": excluded_services,
+            },
             llm_used=False,
             llm_error="LLM not used — no events found." if not LLM_API_KEY else None,
         )
@@ -380,7 +543,7 @@ async def ask_question(body: AskRequest, user: dict = Depends(require_auth)):
         llm_error = "LLM_API_KEY is not configured. Set it in your .env to enable AI narrative summarisation."
     else:
         try:
-            answer = await _call_llm(question, events, total=total)
+            answer = await _call_llm(question, events, total=total, excluded_services=excluded_services)
             llm_used = True
         except Exception as exc:
             llm_error = f"LLM call failed: {exc}"
@@ -388,9 +551,11 @@ async def ask_question(body: AskRequest, user: dict = Depends(require_auth)):
 
     # Fallback: structured summary if LLM unavailable or failed
     if not answer:
-        parts = [f"Found {len(events)} event(s)"]
+        parts = [f"Found {total} event(s)"]
         if entity:
             parts.append(f"related to **{entity}**")
+        if excluded_services:
+            parts.append(f"(excluding {', '.join(excluded_services)})")
         parts.append(f"between {start[:10]} and {end[:10]}.\n")
 
         for i, e in enumerate(events[:10], 1):
@@ -415,6 +580,8 @@ async def ask_question(body: AskRequest, user: dict = Depends(require_auth)):
             "end": end,
             "event_count": len(events),
             "total_matched": total,
+            "services_queried": query_services,
+            "excluded_services": excluded_services,
             "mongo_query": json.dumps(query, default=str),
         },
         llm_used=llm_used,

backend/routes/config.py

@@ -1,4 +1,5 @@
 from config import (
+    AI_FEATURES_ENABLED,
     AUTH_CLIENT_ID,
     AUTH_ENABLED,
     AUTH_SCOPE,
@@ -18,3 +19,10 @@ def auth_config():
         "scope": AUTH_SCOPE,
         "redirect_uri": None,  # frontend uses window.location.origin by default
     }
+
+
+@router.get("/config/features")
+def features_config():
+    return {
+        "ai_features_enabled": AI_FEATURES_ENABLED,
+    }

backend/tests/test_api.py

@@ -1,6 +1,41 @@
 from datetime import UTC, datetime
 
 
+def test_config_features(client):
+    response = client.get("/api/config/features")
+    assert response.status_code == 200
+    data = response.json()
+    assert "ai_features_enabled" in data
+    assert isinstance(data["ai_features_enabled"], bool)
+
+
+def test_ask_disabled_when_ai_features_off():
+    import subprocess
+    import sys
+
+    code = """
+import sys
+sys.path.insert(0, '.')
+import os
+os.environ['AI_FEATURES_ENABLED'] = 'false'
+
+# Re-import config with the env override
+import importlib
+import config
+importlib.reload(config)
+
+# Now import main; it will pick up the new AI_FEATURES_ENABLED
+import main
+ask_paths = [r.path for r in main.app.routes if hasattr(r, 'path') and 'ask' in r.path]
+print('ASK_PATHS:', ask_paths)
+assert len(ask_paths) == 0, f"Expected no ask routes, found: {ask_paths}"
+print('OK')
+"""
+    result = subprocess.run([sys.executable, "-c", code], capture_output=True, text=True, cwd=".")
+    assert result.returncode == 0, f"Subprocess failed: {result.stdout}\n{result.stderr}"
+    assert "OK" in result.stdout
+
+
 def test_health(client):
     response = client.get("/health")
     assert response.status_code == 200

backend/tests/test_ask.py

@@ -236,7 +236,7 @@ class TestAskEndpoint:
             }
         )
 
-        async def fake_llm(question, events, total=None):
+        async def fake_llm(question, events, total=None, excluded_services=None):
             return "The device had a failed wipe attempt."
 
         monkeypatch.setattr("routes.ask.LLM_API_KEY", "fake-key")