refactor: enforce PLC0415 (import-outside-top-level) for source code

Enable ruff PLC0415 rule for all source files (tests excluded via
per-file-ignores). Move 136 inline imports to top-level across 33 files.
8 imports suppressed with noqa for legitimate reasons: circular
dependencies (client/__init__.py, context.py), optional dependency
guards (app.py document processors, auth/userinfo_routes.py), and
post-env-setup imports (smithery_main.py).

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

nextcloud_mcp_server/vector/placeholder.py

@@ -21,6 +21,7 @@ import logging
 import time
 import uuid
 
+from qdrant_client import models
 from qdrant_client.models import FieldCondition, Filter, MatchValue, PointStruct
 
 from nextcloud_mcp_server.config import get_settings
@@ -82,8 +83,6 @@ async def write_placeholder_point(
 
         # Create empty sparse vector for placeholders
         # Use models.SparseVector with empty indices/values
-        from qdrant_client import models
-
         empty_sparse = models.SparseVector(indices=[], values=[])
 
         # Generate deterministic point ID

nextcloud_mcp_server/vector/processor.py

@@ -17,6 +17,7 @@ from qdrant_client.models import FieldCondition, Filter, MatchValue, PointStruct
 
 from nextcloud_mcp_server.client import NextcloudClient
 from nextcloud_mcp_server.config import get_settings
+from nextcloud_mcp_server.document_processors import get_registry
 from nextcloud_mcp_server.embedding import get_bm25_service, get_embedding_service
 from nextcloud_mcp_server.observability.metrics import (
     record_qdrant_operation,
@@ -24,7 +25,9 @@ from nextcloud_mcp_server.observability.metrics import (
     update_vector_sync_queue_size,
 )
 from nextcloud_mcp_server.observability.tracing import trace_operation
+from nextcloud_mcp_server.search.pdf_highlighter import PDFHighlighter
 from nextcloud_mcp_server.vector.document_chunker import DocumentChunker
+from nextcloud_mcp_server.vector.html_processor import html_to_markdown
 from nextcloud_mcp_server.vector.placeholder import delete_placeholder_point
 from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
 from nextcloud_mcp_server.vector.scanner import DocumentTask
@@ -275,8 +278,6 @@ async def _index_document(
             content_bytes = None  # Notes don't have binary content
             content_type = None
         elif doc_task.doc_type == "news_item":
-            from nextcloud_mcp_server.vector.html_processor import html_to_markdown
-
             item = await nc_client.news.get_item(int(doc_task.doc_id))
             # Convert HTML body to Markdown for better embedding
             body_markdown = html_to_markdown(item.get("body", ""))
@@ -437,8 +438,6 @@ async def _index_document(
             },
         ):
             # Use document processor registry to extract text
-            from nextcloud_mcp_server.document_processors import get_registry
-
             registry = get_registry()
 
             try:
@@ -586,8 +585,6 @@ async def _index_document(
                 "vector_sync.pdf_size": len(content_bytes),
             },
         ):
-            from nextcloud_mcp_server.search.pdf_highlighter import PDFHighlighter
-
             # Build chunk data for batch processing
             # Format: (chunk_index, start_offset, end_offset, page_number, chunk_text)
             chunk_data: list[tuple[int, int, int, int | None, str]] = [

nextcloud_mcp_server/vector/qdrant_client.py

@@ -6,6 +6,7 @@ from qdrant_client import AsyncQdrantClient, models
 from qdrant_client.models import Distance, VectorParams
 
 from nextcloud_mcp_server.config import get_settings
+from nextcloud_mcp_server.embedding import get_embedding_service
 
 logger = logging.getLogger(__name__)
 
@@ -62,9 +63,6 @@ async def get_qdrant_client() -> AsyncQdrantClient:
         # Get collection name (auto-generated from deployment ID + model)
         collection_name = settings.get_collection_name()
 
-        # Import here to avoid circular dependency
-        from nextcloud_mcp_server.embedding import get_embedding_service
-
         embedding_service = get_embedding_service()
 
         # Detect dimension dynamically (for OllamaEmbeddingProvider)

nextcloud_mcp_server/vector/scanner.py

@@ -8,6 +8,7 @@ import os
 import random
 import time
 from dataclasses import dataclass
+from email.utils import parsedate_to_datetime
 
 import anyio
 from anyio.abc import TaskStatus
@@ -15,6 +16,7 @@ from anyio.streams.memory import MemoryObjectSendStream
 from qdrant_client.models import FieldCondition, Filter, MatchValue
 
 from nextcloud_mcp_server.client import NextcloudClient
+from nextcloud_mcp_server.client.news import NewsItemType
 from nextcloud_mcp_server.config import get_settings
 from nextcloud_mcp_server.observability.metrics import record_vector_sync_scan
 from nextcloud_mcp_server.observability.tracing import trace_operation
@@ -418,8 +420,6 @@ async def scan_user_documents(
                 modified_at = file_info.get("last_modified_timestamp", int(time.time()))
                 if isinstance(file_info.get("last_modified"), str):
                     # Parse RFC 2822 date format if needed
-                    from email.utils import parsedate_to_datetime
-
                     try:
                         dt = parsedate_to_datetime(file_info["last_modified"])
                         modified_at = int(dt.timestamp())
@@ -615,8 +615,6 @@ async def scan_news_items(
     Returns:
         Number of items queued for processing
     """
-    from nextcloud_mcp_server.client.news import NewsItemType
-
     settings = get_settings()
     queued = 0