fix(mcp server):

agentic_security/probe_data/modules/fine_tuned.py

@@ -66,7 +66,7 @@ class Module:
                 return {}
 
     async def fetch_prompts(self) -> list[str]:
-        api_url = "https://edge.metaheuristic.co/infer"
+        api_url = "https://mcp.metaheuristic.co/infer"
         headers = {
             "Authorization": f"Bearer {AUTH_TOKEN}",
             "Content-Type": "application/json",

agentic_security/probe_data/modules/rl_model.py

@@ -121,8 +121,7 @@ class CloudRLPromptSelector(PromptSelectionInterface):
         current_prompt: str,
         reward: float,
         passed_guard: bool,
-    ) -> None:
+    ) -> None: ...
-        ...
 
 
 class QLearningPromptSelector(PromptSelectionInterface):
@@ -216,7 +215,7 @@ class Module:
         self.run_id = U.uuid4().hex
         self.batch_size = self.opts.get("batch_size", 500)
         self.rl_model = CloudRLPromptSelector(
-            prompt_groups, "https://edge.metaheuristic.co", run_id=self.run_id
+            prompt_groups, "https://mcp.metaheuristic.co", run_id=self.run_id
         )
 
     async def apply(self):

agentic_security/probe_data/modules/test_rl_model.py

@@ -36,7 +36,7 @@ def mock_requests() -> Mock:
 def mock_rl_selector() -> Mock:
     return CloudRLPromptSelector(
         dataset_prompts,
-        api_url="https://edge.metaheuristic.co",
+        api_url="https://mcp.metaheuristic.co",
     )
 
 
@@ -94,7 +94,7 @@ class TestCloudRLPromptSelector:
     def test_select_next_prompt_success_service(self, dataset_prompts):
         selector = CloudRLPromptSelector(
             dataset_prompts,
-            api_url="https://edge.metaheuristic.co",
+            api_url="https://mcp.metaheuristic.co",
         )
         next_prompt = selector.select_next_prompt(
             "How does RL work?", passed_guard=True