Refactor quick_search to support SSE response streaming.

Updated `quick_search` to return an `AsyncGenerator` for streaming server-sent events (SSE) during quick research execution. Enhanced error handling to provide detailed feedback and upgraded `gpt-researcher` dependency to version 0.13.3 for compatibility.

requirements.txt

@@ -1,5 +1,5 @@
 # GPT Researcher dependencies
-gpt-researcher>=0.12.16
+gpt-researcher>=0.13.3
 python-dotenv~=1.1.0
 
 # MCP dependencies

src/phoenix_technologies/gpt_researcher/server.py

@@ -162,7 +162,7 @@ async def deep_research(query: str) -> AsyncGenerator[str, None]:
             "status": "Research completed successfully",
             "source_count": len(sources),
             "context": context,
-            "sources": format_sources_for_response(sources), # Nutze deine Formatierungsfunktion
+            "sources": format_sources_for_response(sources),
             "source_urls": source_urls
         }
 
@@ -185,7 +185,7 @@ async def deep_research(query: str) -> AsyncGenerator[str, None]:
         # oder die Exception weiter werfen, wenn FastMCP das besser handhabt.
 
 @mcp.tool()
-async def quick_search(query: str) -> Dict[str, Any]:
+async def quick_search(query: str) -> AsyncGenerator[str, None]:
     """
     Perform a quick web search on a given query and return search results with snippets.
     This optimizes for speed over quality and is useful when an LLM doesn't need in-depth
@@ -206,19 +206,37 @@ async def quick_search(query: str) -> Dict[str, Any]:
     researcher = GPTResearcher(query)
 
     try:
+        yield format_sse_event(
+            "message",
+            {"research_id": search_id, "query": query, "status": "Research initiated"}
+        )
+
         # Perform quick search
         search_results = await researcher.quick_search(query=query)
         mcp.researchers[search_id] = researcher
         logger.info(f"Quick search completed for ID: {search_id}")
 
-        return create_success_response({
+        final_data_payload = {
             "search_id": search_id,
             "query": query,
             "result_count": len(search_results) if search_results else 0,
             "search_results": search_results
-        })
+        }
+
+        # Sende das finale Ergebnis als 'tool_result' Event
+        yield format_sse_event("message", final_data_payload)
+        logger.info(f"Sent final research result for ID: {search_id}")
     except Exception as e:
-        return handle_exception(e, "Quick search")
+        logger.error(f"Error during deep_research for query '{query}': {e}", exc_info=True)
+        # Sende ein Fehler-Event an den Client
+        error_payload = {
+            "research_id": search_id, # Kann None sein, wenn Fehler sehr früh auftritt
+            "query": query,
+            "status": "Error occurred",
+            "error_message": str(e),
+            "error_details": "Check server logs for more information."
+        }
+        yield format_sse_event("message", error_payload)
 
 
 @mcp.tool()