Merge pull request #39 from VirtualFlyBrain/paper-test-fixes

Paper test fixes

Author: Clare72

src/vfbquery/vfb_queries.py

@@ -291,6 +291,11 @@ class TermInfoOutputSchema(Schema):
     Meta = fields.Dict(keys=fields.String(), values=fields.String(), required=True)
     Tags = fields.List(fields.String(), required=True)
     Queries = fields.List(QueryField(), required=False)
+    # RelatedTools: MCP tools (other than run_query) that are useful for this entity.
+    # Each entry: {"tool": "<tool_name>", "label": "...", "default_args": {...}}.
+    # Distinct from Queries because these are not dispatched via run_query — the
+    # client should call the named tool directly with default_args.
+    RelatedTools = fields.List(fields.Dict(), required=False)
     IsIndividual = fields.Bool(missing=False, required=False)
     Images = fields.Dict(keys=fields.String(), values=fields.List(fields.Nested(ImageSchema()), missing={}), required=False, allow_none=True)
     IsClass = fields.Bool(missing=False, required=False)
@@ -400,6 +405,7 @@ def term_info_parse_object(results, short_form):
     termInfo["SuperTypes"] = []
     termInfo["Tags"] = []
     termInfo["Queries"] = []
+    termInfo["RelatedTools"] = []
     termInfo["IsClass"] = False
     termInfo["IsIndividual"] = False
     termInfo["IsTemplate"] = False
@@ -740,8 +746,9 @@ def term_info_parse_object(results, short_form):
 
         # NeuronsPartHere query - for Class+Anatomy terms (synaptic neuropils, etc.)
         # Matches XMI criteria: Class + Synaptic_neuropil, or other anatomical regions
-        if contains_all_tags(termInfo["SuperTypes"], ["Class"]) and (
-            "Synaptic_neuropil" in termInfo["SuperTypes"] or 
+        # Excluded for neuron classes: "neurons with some part in <a neuron>" is not a meaningful query
+        if contains_all_tags(termInfo["SuperTypes"], ["Class"]) and "Neuron" not in termInfo["SuperTypes"] and (
+            "Synaptic_neuropil" in termInfo["SuperTypes"] or
             "Anatomy" in termInfo["SuperTypes"]
         ):
             q = NeuronsPartHere_to_schema(termInfo["Name"], {"short_form": vfbTerm.term.core.short_form})
@@ -783,9 +790,11 @@ def term_info_parse_object(results, short_form):
             q = ComponentsOf_to_schema(termInfo["Name"], {"short_form": vfbTerm.term.core.short_form})
             queries.append(q)
 
-        # PartsOf query - for any Class
+        # PartsOf query - for any Class except neuron classes
         # Matches XMI criteria: Class (any)
-        if contains_all_tags(termInfo["SuperTypes"], ["Class"]):
+        # Excluded for neuron classes: anatomical sub-parts of a neuron type are not modelled
+        # in the ontology in a way that makes this query useful at the class level.
+        if contains_all_tags(termInfo["SuperTypes"], ["Class"]) and "Neuron" not in termInfo["SuperTypes"]:
             q = PartsOf_to_schema(termInfo["Name"], {"short_form": vfbTerm.term.core.short_form})
             queries.append(q)
 
@@ -941,17 +950,72 @@ def term_info_parse_object(results, short_form):
             queries.append(q)
             q = UpstreamClassConnectivity_to_schema(termInfo["Name"], {"short_form": vfbTerm.term.core.short_form})
             queries.append(q)
-        
+
+        # Hierarchy entries — surfaced in RelatedTools, dispatched via the
+        # get_hierarchy MCP tool rather than run_query.
+        sf_for_hier = vfbTerm.term.core.short_form
+        # subclass_of hierarchy is meaningful for cell-type taxonomies.
+        # Gate: Class + Cell (matches the "Cell"-bounded ancestor filter inside
+        # get_hierarchy itself).
+        if termInfo["SuperTypes"] and contains_all_tags(termInfo["SuperTypes"], ["Class", "Cell"]):
+            termInfo["RelatedTools"].append({
+                "tool": "get_hierarchy",
+                "label": f"Cell-type hierarchy of {termInfo['Name']}",
+                "default_args": {
+                    "id": sf_for_hier,
+                    "relationship": "subclass_of",
+                    "direction": "both",
+                    "max_depth": 1,
+                },
+            })
+        # part_of hierarchy is meaningful for nervous-system regions
+        # (brain, neuropils, ganglia, tracts), but NOT for cells/neurons or
+        # non-neural anatomy. Special-case the nervous system root, which lacks
+        # the "Nervous_system" SuperType because it isn't part_of itself.
+        is_ns_region = (
+            termInfo["SuperTypes"]
+            and contains_all_tags(termInfo["SuperTypes"], ["Class", "Nervous_system"])
+            and "Cell" not in termInfo["SuperTypes"]
+        )
+        is_ns_root = sf_for_hier == "FBbt_00005093"
+        if is_ns_region or is_ns_root:
+            termInfo["RelatedTools"].append({
+                "tool": "get_hierarchy",
+                "label": f"Region containment hierarchy of {termInfo['Name']}",
+                "default_args": {
+                    "id": sf_for_hier,
+                    "relationship": "part_of",
+                    "direction": "both",
+                    "max_depth": 1,
+                },
+            })
+
         # FlyBase stock finder — for Feature terms (FBgn/FBal/FBti/FBtp/FBco/FBst)
         sf = vfbTerm.term.core.short_form
         if sf.startswith(("FBgn", "FBal", "FBti", "FBtp", "FBco", "FBst")):
             q = FindStocks_to_schema(termInfo["Name"], {"short_form": sf})
             queries.append(q)
+            # Also surface the dedicated find_stocks MCP tool, which exposes
+            # the optional collection_filter parameter (Bloomington, Kyoto,
+            # VDRC, etc.) that the run_query/FindStocks path does not.
+            termInfo["RelatedTools"].append({
+                "tool": "find_stocks",
+                "label": f"Find fly stocks for {termInfo['Name']} (with optional stock-centre filter)",
+                "default_args": {"feature_id": sf},
+            })
 
         # FlyBase combination publications — for FBco terms
         if sf.startswith("FBco"):
             q = FindComboPublications_to_schema(termInfo["Name"], {"short_form": sf})
             queries.append(q)
+            # Also surface the dedicated find_combo_publications MCP tool,
+            # which returns full per-publication metadata (DOI, PMID, miniref,
+            # year) ready for citation rendering.
+            termInfo["RelatedTools"].append({
+                "tool": "find_combo_publications",
+                "label": f"Find publications for {termInfo['Name']} (with full citation metadata)",
+                "default_args": {"fbco_id": sf},
+            })
 
         # For individuals that are painted domains of anatomical regions, add parent class queries
         if termInfo["IsIndividual"] and termInfo["Technique"] and any('computer' in t.lower() for t in termInfo["Technique"]):