feat(agents): register run_fusion tool for multi-modal disease confidence

src/agents/prompts.py

@@ -7,12 +7,13 @@ from __future__ import annotations
 
 
 ORCHESTRATOR_SYSTEM_PROMPT = """\
-You are the NeuroBridge clinical-ML orchestrator. You have four tools:
+You are the NeuroBridge clinical-ML orchestrator. You have five tools:
 
 - run_bbb_pipeline(smiles, top_k=5)         → for a SMILES molecular string
 - run_eeg_pipeline(input_path)               → for a .fif or .edf EEG file path
 - run_mri_pipeline(input_dir, sites_csv)     → for a directory of NIfTI MRI files
 - retrieve_context(query, k=4)               → for grounding chunks from the knowledge base
+- run_fusion: combine MRI/EEG/clinical-test scores into a per-disease confidence with attribution. Use when the doctor has more than one modality available.
 
 Workflow — follow exactly:
 

src/agents/tools.py

@@ -1,7 +1,7 @@
 """Tool dataclass + registry. Wraps each pipeline + the RAG retriever as a
 function-callable tool the orchestrator can invoke.
 
-Public entry: `build_default_tools(rag_index_dir)` returns the 4 tools.
+Public entry: `build_default_tools(rag_index_dir)` returns the 5 tools.
 """
 from __future__ import annotations
 
@@ -21,6 +21,8 @@ from src.agents.schemas import (
     RetrieveContextInput,
     RetrieveContextOutput,
 )
+from src.fusion.engine import fuse as fuse_engine
+from src.fusion.types import FusionInput, FusionOutput
 from src.core.logger import get_logger
 
 logger = get_logger(__name__)
@@ -175,7 +177,7 @@ def build_default_tools(
     rag_index_dir: Path | None,
     processed_dir: Path = Path("data/processed"),
 ) -> list[Tool]:
-    """Return the 4 tools the orchestrator gets by default."""
+    """Return the 5 tools the orchestrator gets by default."""
     return [
         Tool(
             name="run_bbb_pipeline",
@@ -225,4 +227,17 @@ def build_default_tools(
             output_model=RetrieveContextOutput,
             execute=_make_retrieve_executor(rag_index_dir),
         ),
+        Tool(
+            name="run_fusion",
+            description=(
+                "Combine MRI prediction, EEG prediction, and clinical-test "
+                "scores (MMSE, MoCA, UPDRS, gait, age) into per-disease "
+                "(Alzheimer's, Parkinson's, other) confidence with full "
+                "attribution. Pass whichever modalities are available; "
+                "missing ones are skipped, not imputed. Does NOT use BBB."
+            ),
+            input_model=FusionInput,
+            output_model=FusionOutput,
+            execute=lambda inp: fuse_engine(inp),
+        ),
     ]

tests/agents/test_tools.py

@@ -79,6 +79,7 @@ class TestBuildDefaultTools:
             "run_eeg_pipeline",
             "run_mri_pipeline",
             "retrieve_context",
+            "run_fusion",
         }
 
     def test_each_tool_has_pydantic_input_model(self) -> None:
@@ -115,8 +116,8 @@ class TestBuildDefaultTools:
     def test_default_processed_dir_when_omitted(self) -> None:
         # backwards-compat: omitting processed_dir keeps existing behavior
         tools = build_default_tools(rag_index_dir=None)
-        # just ensure no exception and 4 tools returned
-        assert len(tools) == 4
+        # just ensure no exception and 5 tools returned
+        assert len(tools) == 5
 
     def test_bbb_executor_translates_httpexception_to_valueerror(self) -> None:
         from fastapi import HTTPException

tests/agents/test_tools_fusion.py

@@ -0,0 +1,20 @@
+"""Tests for the run_fusion agent tool."""
+from __future__ import annotations
+
+from src.agents.tools import build_default_tools
+
+
+class TestRunFusionTool:
+    def test_fusion_tool_is_registered(self) -> None:
+        tools = build_default_tools(rag_index_dir=None)
+        names = [t.name for t in tools]
+        assert "run_fusion" in names
+
+    def test_fusion_tool_executes_with_only_clinical(self) -> None:
+        tools = {t.name: t for t in build_default_tools(rag_index_dir=None)}
+        tool = tools["run_fusion"]
+        out = tool.execute(tool.input_model.model_validate({
+            "clinical": {"mmse": 12.0, "age_years": 78.0},
+        }))
+        assert out.top_disease in {"alzheimers", "parkinsons", "other"}
+        assert any(d.disease == "alzheimers" for d in out.diseases)