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>

CLAUDE.md

@@ -224,6 +224,82 @@ docker compose exec db mariadb -u root -ppassword nextcloud -e \
 
 **Testing**: Extract `data["results"]` from MCP responses, not `data` directly.
 
+## MCP Sampling for RAG (ADR-008)
+
+**What is MCP Sampling?**
+MCP sampling allows servers to request LLM completions from their clients. This enables Retrieval-Augmented Generation (RAG) patterns where the server retrieves context and the client's LLM generates answers.
+
+**When to use sampling:**
+- Generating natural language answers from retrieved documents
+- Synthesizing information from multiple sources
+- Creating summaries with citations
+
+**Implementation Pattern** (see ADR-008 for details):
+
+```python
+from mcp.types import ModelHint, ModelPreferences, SamplingMessage, TextContent
+
+@mcp.tool()
+@require_scopes("notes:read")
+async def nc_notes_semantic_search_answer(
+    query: str, ctx: Context, limit: int = 5, max_answer_tokens: int = 500
+) -> SamplingSearchResponse:
+    # 1. Retrieve documents
+    search_response = await nc_notes_semantic_search(query, ctx, limit)
+
+    # 2. Check for no results (don't waste sampling call)
+    if not search_response.results:
+        return SamplingSearchResponse(
+            query=query,
+            generated_answer="No relevant documents found.",
+            sources=[], total_found=0, success=True
+        )
+
+    # 3. Construct prompt with retrieved context
+    prompt = f"{query}\n\nDocuments:\n{format_sources(search_response.results)}\n\nProvide answer with citations."
+
+    # 4. Request LLM completion via sampling
+    try:
+        result = await ctx.session.create_message(
+            messages=[SamplingMessage(role="user", content=TextContent(type="text", text=prompt))],
+            max_tokens=max_answer_tokens,
+            temperature=0.7,
+            model_preferences=ModelPreferences(
+                hints=[ModelHint(name="claude-3-5-sonnet")],
+                intelligencePriority=0.8,
+                speedPriority=0.5,
+            ),
+            include_context="thisServer",
+        )
+
+        return SamplingSearchResponse(
+            query=query,
+            generated_answer=result.content.text,
+            sources=search_response.results,
+            model_used=result.model,
+            stop_reason=result.stopReason,
+            success=True
+        )
+    except Exception as e:
+        # Fallback: Return documents without generated answer
+        return SamplingSearchResponse(
+            query=query,
+            generated_answer=f"[Sampling unavailable: {e}]\n\nFound {len(search_response.results)} documents.",
+            sources=search_response.results,
+            search_method="semantic_sampling_fallback",
+            success=True
+        )
+```
+
+**Key Points**:
+- **No server-side LLM**: Server has no API keys, client controls which model is used
+- **Graceful degradation**: Tool always returns useful results even if sampling fails
+- **User control**: MCP clients SHOULD prompt users to approve sampling requests
+- **No results optimization**: Skip sampling call when no documents found
+- **Fixed prompts**: Prompts are not user-configurable to avoid injection risks
+
+**Reference**: See `nc_notes_semantic_search_answer` in `nextcloud_mcp_server/server/notes.py:517` and ADR-008 for complete implementation.
+
 ## Testing Best Practices (MANDATORY)
 
 ### Always Run Tests