chore: import upstream snapshot with attribution

contributing/samples/integrations/api_registry_agent/agent.py

@@ -0,0 +1,45 @@
+# Copyright 2026 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+
+from google.adk.agents.llm_agent import LlmAgent
+from google.adk.integrations.api_registry import ApiRegistry
+
+# TODO: Fill in with your GCloud project id and MCP server name
+PROJECT_ID = "your-google-cloud-project-id"
+MCP_SERVER_NAME = "your-mcp-server-name"
+
+api_registry = ApiRegistry(PROJECT_ID)
+registry_tools = api_registry.get_toolset(
+    mcp_server_name=MCP_SERVER_NAME,
+)
+root_agent = LlmAgent(
+    name="bigquery_assistant",
+    instruction=f"""
+You are a helpful data analyst assistant with access to BigQuery. The project ID is: {PROJECT_ID}
+
+When users ask about data:
+- Use the project ID {PROJECT_ID} when calling BigQuery tools.
+- First, explore available datasets and tables to understand what data exists.
+- Check table schemas to understand the structure before querying.
+- Write clear, efficient SQL queries to answer their questions.
+- Explain your findings in simple, non-technical language.
+
+Mandatory Requirements:
+- Always use the BigQuery tools to fetch real data rather than making assumptions.
+- For all BigQuery operations, use project_id: {PROJECT_ID}.
+    """,
+    tools=[registry_tools],
+)