feat: implement MCP sampling for semantic search RAG (ADR-008)

Add nc_notes_semantic_search_answer tool that combines semantic search
with MCP sampling to generate natural language answers from retrieved
Nextcloud Notes. This enables Retrieval-Augmented Generation (RAG)
patterns without requiring a server-side LLM.

Key features:
- Client-side LLM generation via ctx.session.create_message()
- Graceful fallback when sampling unavailable
- Proper source citations in generated answers
- No results optimization (skips sampling when no docs found)
- Comprehensive unit and integration tests

Implementation details:
- SamplingSearchResponse model with generated_answer and sources
- Fixed prompt template with document context and citation instructions
- Model preferences hint Claude Sonnet for balanced performance
- Falls back to returning documents without answer on sampling failure

Updates:
- Add ADR-008 documenting sampling architecture decision
- Add MCP sampling pattern guidance to CLAUDE.md
- Update README.md and docs/notes.md (7 → 9 tools)
- Add 4 unit tests and 6 integration tests

🤖 Generated with [Claude Code](https://claude.com/claude-code)

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

nextcloud_mcp_server/models/notes.py

@@ -108,3 +108,41 @@ class SemanticSearchNotesResponse(BaseResponse):
     search_method: str = Field(
         default="semantic", description="Search method used (semantic or hybrid)"
     )
+
+
+class SamplingSearchResponse(BaseResponse):
+    """Response from semantic search with LLM-generated answer via MCP sampling.
+
+    This response includes both a generated natural language answer (created by
+    the MCP client's LLM via sampling) and the source documents used to generate
+    that answer. Users can read the answer for quick information and review
+    sources for verification and deeper exploration.
+
+    Attributes:
+        query: The original user query
+        generated_answer: Natural language answer generated by client's LLM
+        sources: List of semantic search results used as context
+        total_found: Total number of matching documents found
+        search_method: Always "semantic_sampling" for this response type
+        model_used: Name of model that generated the answer (e.g., "claude-3-5-sonnet")
+        stop_reason: Why generation stopped ("endTurn", "maxTokens", etc.)
+    """
+
+    query: str = Field(..., description="Original user query")
+    generated_answer: str = Field(
+        ..., description="LLM-generated answer based on retrieved documents"
+    )
+    sources: List[SemanticSearchResult] = Field(
+        default_factory=list,
+        description="Source documents with excerpts and relevance scores",
+    )
+    total_found: int = Field(..., description="Total matching documents")
+    search_method: str = Field(
+        default="semantic_sampling", description="Search method used"
+    )
+    model_used: Optional[str] = Field(
+        default=None, description="Model that generated the answer"
+    )
+    stop_reason: Optional[str] = Field(
+        default=None, description="Reason generation stopped"
+    )