Mcp enhancements (#43)

* reuses MCP connections in each worker for efficiency

* you can see your food

* you can see your food

* prompt eng around images

scripts/run_worker.py

@@ -12,6 +12,7 @@ from activities.tool_activities import (
     mcp_list_tools,
 )
 from shared.config import TEMPORAL_TASK_QUEUE, get_temporal_client
+from shared.mcp_client_manager import MCPClientManager
 from workflows.agent_goal_workflow import AgentGoalWorkflow
 
 
@@ -23,11 +24,14 @@ async def main():
     llm_model = os.environ.get("LLM_MODEL", "openai/gpt-4")
     print(f"Worker will use LLM model: {llm_model}")
 
+    # Create shared MCP client manager
+    mcp_client_manager = MCPClientManager()
+
     # Create the client
     client = await get_temporal_client()
 
-    # Initialize the activities class
-    activities = ToolActivities()
+    # Initialize the activities class with injected manager
+    activities = ToolActivities(mcp_client_manager)
     print(f"ToolActivities initialized with LLM model: {llm_model}")
 
     # If using Ollama, pre-load the model to avoid cold start latency
@@ -54,25 +58,31 @@ async def main():
     print("Worker ready to process tasks!")
     logging.basicConfig(level=logging.INFO)
 
-    # Run the worker
-    with concurrent.futures.ThreadPoolExecutor(max_workers=100) as activity_executor:
-        worker = Worker(
-            client,
-            task_queue=TEMPORAL_TASK_QUEUE,
-            workflows=[AgentGoalWorkflow],
-            activities=[
-                activities.agent_validatePrompt,
-                activities.agent_toolPlanner,
-                activities.get_wf_env_vars,
-                activities.mcp_tool_activity,
-                dynamic_tool_activity,
-                mcp_list_tools,
-            ],
-            activity_executor=activity_executor,
-        )
+    # Run the worker with proper cleanup
+    try:
+        with concurrent.futures.ThreadPoolExecutor(
+            max_workers=100
+        ) as activity_executor:
+            worker = Worker(
+                client,
+                task_queue=TEMPORAL_TASK_QUEUE,
+                workflows=[AgentGoalWorkflow],
+                activities=[
+                    activities.agent_validatePrompt,
+                    activities.agent_toolPlanner,
+                    activities.get_wf_env_vars,
+                    activities.mcp_tool_activity,
+                    dynamic_tool_activity,
+                    mcp_list_tools,
+                ],
+                activity_executor=activity_executor,
+            )
 
-        print(f"Starting worker, connecting to task queue: {TEMPORAL_TASK_QUEUE}")
-        await worker.run()
+            print(f"Starting worker, connecting to task queue: {TEMPORAL_TASK_QUEUE}")
+            await worker.run()
+    finally:
+        # Cleanup MCP connections when worker shuts down
+        await mcp_client_manager.cleanup()
 
 
 if __name__ == "__main__":