main.py

@@ -447,8 +447,11 @@ async def lifespan(_server: FastMCP) -> AsyncIterator[AppContext]:
             location=cfg.location,
         )
 
-        # Validate credentials by testing a simple operation
+        # Validate credentials and configuration by testing actual resources
-        log_structured_entry("Validating credentials with test embedding", "INFO")
+        log_structured_entry("Starting validation of credentials and resources", "INFO")
+
+        # 1. Validate GenAI embedding access
+        log_structured_entry("Validating GenAI embedding access", "INFO")
         try:
             test_response = await genai_client.aio.models.embed_content(
                 model=cfg.embedding_model,
@@ -459,24 +462,139 @@ async def lifespan(_server: FastMCP) -> AsyncIterator[AppContext]:
             )
             if test_response and test_response.embeddings:
                 log_structured_entry(
-                    "Credentials validated successfully",
+                    "GenAI embedding validation successful",
                     "INFO",
                     {"embedding_dimension": len(test_response.embeddings[0].values)}
                 )
             else:
-                log_structured_entry(
+                msg = "Embedding validation returned empty response"
-                    "Credential validation returned empty response",
+                log_structured_entry(msg, "ERROR")
-                    "WARNING"
+                raise RuntimeError(msg)
-                )
         except Exception as e:
             log_structured_entry(
-                "Failed to validate credentials - embedding test failed",
+                "Failed to validate GenAI embedding access",
                 "ERROR",
                 {"error": str(e), "error_type": type(e).__name__}
             )
             raise
 
-        log_structured_entry("MCP server initialization complete", "INFO")
+        # 2. Validate GCS bucket access
+        log_structured_entry(
+            "Validating GCS bucket access",
+            "INFO",
+            {"bucket": cfg.bucket}
+        )
+        try:
+            session = vs.storage._get_aio_session()
+            token_obj = Token(
+                session=session,
+                scopes=["https://www.googleapis.com/auth/cloud-platform"],
+            )
+            access_token = await token_obj.get()
+            headers = {"Authorization": f"Bearer {access_token}"}
+
+            async with session.get(
+                f"https://storage.googleapis.com/storage/v1/b/{cfg.bucket}/o?maxResults=1",
+                headers=headers,
+            ) as response:
+                if response.status == 403:
+                    msg = f"Access denied to bucket '{cfg.bucket}'. Check permissions."
+                    log_structured_entry(
+                        "GCS bucket validation failed - access denied",
+                        "ERROR",
+                        {"bucket": cfg.bucket, "status": response.status}
+                    )
+                    raise RuntimeError(msg)
+                elif response.status == 404:
+                    msg = f"Bucket '{cfg.bucket}' not found. Check bucket name and project."
+                    log_structured_entry(
+                        "GCS bucket validation failed - not found",
+                        "ERROR",
+                        {"bucket": cfg.bucket, "status": response.status}
+                    )
+                    raise RuntimeError(msg)
+                elif not response.ok:
+                    body = await response.text()
+                    msg = f"Failed to access bucket '{cfg.bucket}': {response.status} - {body}"
+                    log_structured_entry(
+                        "GCS bucket validation failed",
+                        "ERROR",
+                        {"bucket": cfg.bucket, "status": response.status, "response": body}
+                    )
+                    raise RuntimeError(msg)
+
+            log_structured_entry(
+                "GCS bucket validation successful",
+                "INFO",
+                {"bucket": cfg.bucket}
+            )
+        except RuntimeError:
+            raise
+        except Exception as e:
+            log_structured_entry(
+                "Failed to validate GCS bucket access",
+                "ERROR",
+                {"error": str(e), "error_type": type(e).__name__, "bucket": cfg.bucket}
+            )
+            raise
+
+        # 3. Validate vector search endpoint access
+        log_structured_entry(
+            "Validating vector search endpoint access",
+            "INFO",
+            {"endpoint_name": cfg.endpoint_name}
+        )
+        try:
+            # Try to get endpoint info
+            headers = await vs._async_get_auth_headers()
+            session = vs._get_aio_session()
+            endpoint_url = (
+                f"https://{cfg.location}-aiplatform.googleapis.com/v1/{cfg.endpoint_name}"
+            )
+
+            async with session.get(endpoint_url, headers=headers) as response:
+                if response.status == 403:
+                    msg = f"Access denied to endpoint '{cfg.endpoint_name}'. Check permissions."
+                    log_structured_entry(
+                        "Vector search endpoint validation failed - access denied",
+                        "ERROR",
+                        {"endpoint": cfg.endpoint_name, "status": response.status}
+                    )
+                    raise RuntimeError(msg)
+                elif response.status == 404:
+                    msg = f"Endpoint '{cfg.endpoint_name}' not found. Check endpoint name and project."
+                    log_structured_entry(
+                        "Vector search endpoint validation failed - not found",
+                        "ERROR",
+                        {"endpoint": cfg.endpoint_name, "status": response.status}
+                    )
+                    raise RuntimeError(msg)
+                elif not response.ok:
+                    body = await response.text()
+                    msg = f"Failed to access endpoint '{cfg.endpoint_name}': {response.status} - {body}"
+                    log_structured_entry(
+                        "Vector search endpoint validation failed",
+                        "ERROR",
+                        {"endpoint": cfg.endpoint_name, "status": response.status, "response": body}
+                    )
+                    raise RuntimeError(msg)
+
+            log_structured_entry(
+                "Vector search endpoint validation successful",
+                "INFO",
+                {"endpoint": cfg.endpoint_name}
+            )
+        except RuntimeError:
+            raise
+        except Exception as e:
+            log_structured_entry(
+                "Failed to validate vector search endpoint access",
+                "ERROR",
+                {"error": str(e), "error_type": type(e).__name__, "endpoint": cfg.endpoint_name}
+            )
+            raise
+
+        log_structured_entry("All validations passed - MCP server initialization complete", "INFO")
 
         yield AppContext(
             vector_search=vs,