diff --git a/contributing/samples/bigquery/agent.py b/contributing/samples/bigquery/agent.py
index 3cd1eb99..c1b265c0 100644
--- a/contributing/samples/bigquery/agent.py
+++ b/contributing/samples/bigquery/agent.py
@@ -60,7 +60,7 @@ bigquery_toolset = BigQueryToolset(
 # debug CLI
 root_agent = llm_agent.Agent(
     model="gemini-2.0-flash",
-    name="hello_agent",
+    name="bigquery_agent",
     description=(
         "Agent to answer questions about BigQuery data and models and execute"
         " SQL queries."
diff --git a/contributing/samples/bigquery_agent/README.md b/contributing/samples/google_api/README.md
similarity index 50%
rename from contributing/samples/bigquery_agent/README.md
rename to contributing/samples/google_api/README.md
index c7dc7fd8..c1e6e8d4 100644
--- a/contributing/samples/bigquery_agent/README.md
+++ b/contributing/samples/google_api/README.md
@@ -1,45 +1,40 @@
-# BigQuery Sample
+# Google API Tools Sample
 
 ## Introduction
 
-This sample tests and demos the BigQuery support in ADK via two tools:
+This sample tests and demos Google API tools available in the
+`google.adk.tools.google_api_tool` module. We pick the following BigQuery API
+tools for this sample agent:
 
-* 1. bigquery_datasets_list:
+1. `bigquery_datasets_list`: List user's datasets.
 
-    List user's datasets.
+2. `bigquery_datasets_get`: Get a dataset's details.
 
-* 2. bigquery_datasets_get:
-    Get a dataset's details.
+3. `bigquery_datasets_insert`: Create a new dataset.
 
-* 3. bigquery_datasets_insert:
-    Create a new dataset.
+4. `bigquery_tables_list`: List all tables in a dataset.
 
-* 4. bigquery_tables_list:
-    List all tables in a dataset.
+5. `bigquery_tables_get`: Get a table's details.
 
-* 5. bigquery_tables_get:
-    Get a table's details.
-
-* 6. bigquery_tables_insert:
-    Insert a new table into a dataset.
+6. `bigquery_tables_insert`: Insert a new table into a dataset.
 
 ## How to use
 
-* 1. Follow https://developers.google.com/identity/protocols/oauth2#1.-obtain-oauth-2.0-credentials-from-the-dynamic_data.setvar.console_name. to get your client id and client secret.
+1. Follow https://developers.google.com/identity/protocols/oauth2#1.-obtain-oauth-2.0-credentials-from-the-dynamic_data.setvar.console_name. to get your client id and client secret.
   Be sure to choose "web" as your client type.
 
-* 2. Configure your `.env` file to add two variables:
+2. Configure your `.env` file to add two variables:
 
   * OAUTH_CLIENT_ID={your client id}
   * OAUTH_CLIENT_SECRET={your client secret}
 
   Note: don't create a separate `.env` file , instead put it to the same `.env` file that stores your Vertex AI or Dev ML credentials
 
-* 3. Follow https://developers.google.com/identity/protocols/oauth2/web-server#creatingcred to add http://localhost/dev-ui/ to "Authorized redirect URIs".
+3. Follow https://developers.google.com/identity/protocols/oauth2/web-server#creatingcred to add http://localhost/dev-ui/ to "Authorized redirect URIs".
 
   Note: localhost here is just a hostname that you use to access the dev ui, replace it with the actual hostname you use to access the dev ui.
 
-* 4. For 1st run, allow popup for localhost in Chrome.
+4. For 1st run, allow popup for localhost in Chrome.
 
 ## Sample prompt
 
diff --git a/contributing/samples/bigquery_agent/__init__.py b/contributing/samples/google_api/__init__.py
similarity index 100%
rename from contributing/samples/bigquery_agent/__init__.py
rename to contributing/samples/google_api/__init__.py
diff --git a/contributing/samples/bigquery_agent/agent.py b/contributing/samples/google_api/agent.py
similarity index 98%
rename from contributing/samples/bigquery_agent/agent.py
rename to contributing/samples/google_api/agent.py
index 976cea17..1cdbab9c 100644
--- a/contributing/samples/bigquery_agent/agent.py
+++ b/contributing/samples/google_api/agent.py
@@ -40,7 +40,7 @@ bigquery_toolset = BigQueryToolset(
 
 root_agent = Agent(
     model="gemini-2.0-flash",
-    name="bigquery_agent",
+    name="google_api_bigquery_agent",
     instruction="""
       You are a helpful Google BigQuery agent that help to manage users' data on Google BigQuery.
       Use the provided tools to conduct various operations on users' data in Google BigQuery.