fix(tools): score_project_match strips 'project' filler + uses title for SequenceMatcher

CI surfaced three issues:
- 'famous supply project' didn't substring-match 'Famous-Supply Work topics'
  because the trailing filler word 'project' blocked the substring tier.
  Strip {project, projects} from the query before the substring check.
- SequenceMatcher fallback against `combined` (title + description +
  summary) diluted ratios to ~0.5 for plausible matches. Use title
  directly; the 0.70 tier already handles description/summary mentions.
- Test patches used patch.object on a consumer module where
  list_projects is imported locally — patch the source module instead.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

src/fabledassistant/services/tools/_helpers.py

@@ -22,21 +22,33 @@ def schedule_embedding(note_id: int, user_id: int, title: str, body: str) -> Non
         asyncio.create_task(upsert_note_embedding(note_id, user_id, text))
 
 
+_PROJECT_QUERY_NOISE = {"project", "projects"}
+
+
 def _normalize(s: str) -> str:
     """Lowercase and collapse non-alphanumerics to single spaces."""
     return re.sub(r"[^a-z0-9]+", " ", s.lower()).strip()
 
 
+def _normalize_query(query: str) -> str:
+    """Normalize plus drop trailing type-nouns ('project' / 'projects')
+    that users add as filler when referring to a project by name."""
+    tokens = [t for t in _normalize(query).split() if t not in _PROJECT_QUERY_NOISE]
+    return " ".join(tokens)
+
+
 def score_project_match(query: str, project) -> float:
     """Score how well `query` matches `project`. Range [0.0, 1.0].
 
     Tiered: exact title → 1.0, substring either-way → 0.85, query found in
-    description/summary → 0.70, otherwise SequenceMatcher ratio. Substring
-    tiers exist because LLM-generated colloquial queries (e.g. "famous
-    supply project" for "Famous-Supply Work topics") would otherwise score
-    too low under pure SequenceMatcher and be treated as no match.
+    description/summary → 0.70, otherwise SequenceMatcher ratio against the
+    title. Substring tiers exist because LLM-generated colloquial queries
+    (e.g. "famous supply project" for "Famous-Supply Work topics") would
+    otherwise score too low under pure SequenceMatcher and be treated as
+    no match. Filler words like "project" are stripped from the query so
+    the substring check still fires.
     """
-    q = _normalize(query)
+    q = _normalize_query(query)
     if not q:
         return 0.0
     title = _normalize(project.title)
@@ -50,7 +62,10 @@ def score_project_match(query: str, project) -> float:
         return 0.85
     if q in combined:
         return 0.70
-    return SequenceMatcher(None, q, combined).ratio()
+    # SequenceMatcher against the title — comparing against `combined`
+    # dilutes the ratio with long description/summary text and produces
+    # uniformly low scores even for plausible matches.
+    return SequenceMatcher(None, q, title).ratio()
 
 
 async def resolve_project(user_id: int, project_name: str):

tests/test_tool_use_fixes.py

@@ -20,7 +20,9 @@ async def test_search_projects_tool_returns_success_true():
 
     fake_projects = [_project(5, "Famous-Supply Work topics", "AT&T fiber circuit")]
 
-    with patch.object(projects_tool, "list_projects", new=AsyncMock(return_value=fake_projects)):
+    # `list_projects` is imported locally inside search_projects_tool, so we
+    # patch the source module rather than the consumer.
+    with patch("fabledassistant.services.projects.list_projects", new=AsyncMock(return_value=fake_projects)):
         result = await projects_tool.search_projects_tool(
             user_id=1, arguments={"query": "famous supply"},
         )
@@ -101,7 +103,7 @@ async def test_search_projects_tool_ranks_substring_match_above_others():
         _project(13, "ImageRepo", auto_summary="self-hosted Flask app"),
     ]
 
-    with patch.object(projects_tool, "list_projects", new=AsyncMock(return_value=fake_projects)):
+    with patch("fabledassistant.services.projects.list_projects", new=AsyncMock(return_value=fake_projects)):
         result = await projects_tool.search_projects_tool(
             user_id=1, arguments={"query": "famous supply project"},
         )