feat(server): integrate MCP queries and investigation messaging

Add project-bound OAuth MCP queries and controlled analysis with scoped evidence,
limits, timeouts and a nearby-model spatial query using EPSG:4326 inputs.
Default nearby searches to 200 meters and five results.

Integrate investigation messaging/presentation, health/readiness and typed history;
remove duplicate web search and fix online simulation and compressed baseline writes.
Include project migrations 023-025; keep local credentials and demo seeding excluded.

Validation: 319 focused pytest checks; real PostGIS temporary-table regression;
read-only Jinqian bridge radius comparison; cross-language MCP discovery/calls.

app/services/sensor_placement.py

@@ -82,8 +82,16 @@ def _create_validated_placement(
     sensor_locations: list[str],
     sensor_type: str = "pressure",
     method: str | None = None,
+    persist_results: bool = True,
 ) -> dict[str, Any]:
     validate_sensor_placement_nodes(project_code, sensor_locations)
+    if not persist_results:
+        return {
+            "sensor_locations": sensor_locations,
+            "sensor_points": _sensor_points(project_code, sensor_locations),
+            "method": method,
+            "sensor_type": sensor_type,
+        }
     return sensor_placement_repository.create_sensor_placement(
         project_code,
         run_name=run_name,
@@ -102,6 +110,7 @@ def optimize_sensor_placement_by_sensitivity(
     min_diameter: int,
     created_by: str,
     sensor_type: str = "pressure",
+    persist_results: bool = True,
 ) -> dict[str, Any]:
     """Run sensitivity placement and persist the validated result."""
 
@@ -119,6 +128,7 @@ def optimize_sensor_placement_by_sensitivity(
         sensor_locations=sensor_locations,
         sensor_type=sensor_type,
         method="sensitivity",
+        persist_results=persist_results,
     )
 
 
@@ -129,6 +139,7 @@ def optimize_sensor_placement_by_kmeans(
     min_diameter: int,
     created_by: str,
     sensor_type: str = "pressure",
+    persist_results: bool = True,
 ) -> dict[str, Any]:
     """Export the model, run K-means placement, and persist the result."""
 
@@ -146,6 +157,7 @@ def optimize_sensor_placement_by_kmeans(
         sensor_locations=sensor_locations,
         sensor_type=sensor_type,
         method="kmeans",
+        persist_results=persist_results,
     )