Switch default model to qwen3 and add intent routing for reliable tool calling

Mistral didn't reliably use Ollama's structured tool calling API — it wrote
tool calls as JSON text instead of invoking them. This adds an intent routing
layer that classifies user intent via a fast non-streaming LLM call before
streaming, executing detected tools directly and bypassing native tool calling.

- Change default OLLAMA_MODEL from mistral to qwen3
- Add intent.py: classify_intent() with JSON parsing and fallback regex
- Integrate intent routing into generation_task.py round 0
- Add all-day event support (iCalendar DATE values) to CalDAV service
- Add recurring event support (RRULE) to CalDAV service and tool definition
- Improve create_event tool description for descriptive titles
- Enhance system prompt with structured tool usage guidance

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

src/fabledassistant/services/generation_task.py

@@ -16,6 +16,7 @@ from fabledassistant.models.conversation import Message
 from fabledassistant.services.generation_buffer import GenerationBuffer, GenerationState
 from fabledassistant.services.llm import ChatChunk, generate_completion, stream_chat, stream_chat_with_tools
 from fabledassistant.services.chat import update_conversation_title
+from fabledassistant.services.intent import classify_intent
 from fabledassistant.services.tools import get_tools_for_user, execute_tool
 
 logger = logging.getLogger(__name__)
@@ -102,6 +103,37 @@ async def run_generation(
             round_tool_calls: list[dict] = []
             logger.info("Generation round %d started for conv %d (model=%s)", _round, conv_id, model)
 
+            # Intent routing — first round only
+            if _round == 0 and tools:
+                intent = await classify_intent(user_content, tools, model)
+                if intent.tool_name:
+                    logger.info("Intent router detected tool: %s(%s)", intent.tool_name, json.dumps(intent.arguments)[:200])
+                    result = await execute_tool(user_id, intent.tool_name, intent.arguments)
+                    logger.info("Intent-routed tool %s result: success=%s", intent.tool_name, result.get("success"))
+
+                    tool_record = {
+                        "function": intent.tool_name,
+                        "arguments": intent.arguments,
+                        "result": result,
+                        "status": "success" if result.get("success") else "error",
+                    }
+                    all_tool_calls.append(tool_record)
+                    buf.append_event("tool_call", {"tool_call": tool_record})
+
+                    # Inject into messages so LLM can write a natural response
+                    messages.append({
+                        "role": "assistant",
+                        "content": "",
+                        "tool_calls": [
+                            {"function": {"name": intent.tool_name, "arguments": intent.arguments}}
+                        ],
+                    })
+                    messages.append({
+                        "role": "tool",
+                        "content": json.dumps(result),
+                    })
+                    continue  # Next round: LLM streams response incorporating result
+
             async for chunk in stream_chat_with_tools(messages, model, tools=tools):
                 if buf.cancel_event.is_set():
                     cancelled = True