fix: enforce patient's phase preference in API call, not LLM guidance

Previously the research LLM was expected to forward profile.phases to
the search tool — an unreliable pattern. Now the code sets phases
directly from profile.phases for INTERVENTIONAL searches, ignoring
whatever the LLM passed. EAP and OBSERVATIONAL searches are unaffected
(phase filters don't apply there).

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

agents/research.py

@@ -108,8 +108,10 @@ def run_research_agent(client: anthropic.Anthropic, profile: PatientProfile) ->
                 continue
             args = block.input
             radius = args.get("radius_miles", profile.radius_miles)
-            phases = args.get("phases") or None
             study_type = args.get("study_type", "INTERVENTIONAL")
+            # Enforce patient's phase preference; don't rely on LLM to repeat it.
+            # Phase filters only apply to INTERVENTIONAL searches.
+            phases = (profile.phases or None) if study_type == "INTERVENTIONAL" else None
             status_msg = (
                 f"[cyan]Searching:[/cyan] '[bold]{args['condition']}[/bold]' | "
                 f"radius=[bold]{radius}[/bold] mi | "
@@ -191,6 +193,9 @@ def stream_research_agent(
             args = block.input
             radius = args.get("radius_miles", profile.radius_miles)
             study_type = args.get("study_type", "INTERVENTIONAL")
+            # Enforce patient's phase preference; don't rely on LLM to repeat it.
+            # Phase filters only apply to INTERVENTIONAL searches.
+            phases = (profile.phases or None) if study_type == "INTERVENTIONAL" else None
             type_label = {"INTERVENTIONAL": "clinical trials", "EXPANDED_ACCESS": "expanded access programs", "OBSERVATIONAL": "observational studies"}.get(study_type, study_type.lower())
             yield ("status", f"Searching ClinicalTrials.gov for **{args['condition']}** ({type_label}, {radius} mi radius)…")
             try:
@@ -199,7 +204,7 @@ def stream_research_agent(
                     lat=args["lat"],
                     lon=args["lon"],
                     radius_miles=radius,
-                    phases=args.get("phases") or None,
+                    phases=phases,
                     study_type=study_type,
                 )
                 ranked = _flatten_and_rank(studies, profile.lat, profile.lon)

tests/agents/test_research.py

@@ -150,6 +150,41 @@ class TestRunResearchAgent:
 
         assert result == "Done."
 
+    def test_profile_phases_enforced_in_interventional_search(self, mock_client, als_patient):
+        """Patient's phase preference is passed to search_trials_api, not whatever LLM chose."""
+        patient_with_phases = als_patient.__class__(
+            **{**als_patient.__dict__, "phases": ["3", "4"]}
+        )
+        # LLM passes no phases in its tool call args
+        search_msg = make_message(content=[_search_block()], stop_reason="tool_use")
+        final_msg = make_message(content=[make_text_block("Done.")], stop_reason="end_turn")
+        mock_client.messages.create.side_effect = [search_msg, final_msg]
+
+        with patch(PATCH_SEARCH, return_value=[]) as ms, \
+             patch(PATCH_FLATTEN, return_value=[]), \
+             patch(PATCH_BULK, return_value=[]):
+            run_research_agent(mock_client, patient_with_phases)
+
+        _, kwargs = ms.call_args
+        assert kwargs["phases"] == ["3", "4"]
+
+    def test_no_phase_filter_when_profile_phases_empty(self, mock_client, als_patient):
+        """No phase filter applied when patient has no phase preference."""
+        patient_no_phases = als_patient.__class__(
+            **{**als_patient.__dict__, "phases": []}
+        )
+        search_msg = make_message(content=[_search_block()], stop_reason="tool_use")
+        final_msg = make_message(content=[make_text_block("Done.")], stop_reason="end_turn")
+        mock_client.messages.create.side_effect = [search_msg, final_msg]
+
+        with patch(PATCH_SEARCH, return_value=[]) as ms, \
+             patch(PATCH_FLATTEN, return_value=[]), \
+             patch(PATCH_BULK, return_value=[]):
+            run_research_agent(mock_client, patient_no_phases)
+
+        _, kwargs = ms.call_args
+        assert kwargs["phases"] is None
+
     def test_search_results_serialized_into_tool_result(self, mock_client, als_patient):
         """Verify flatten output flows through bulk_parse and into the tool-result message."""
         import json as _json