diff --git a/contributing/samples/application_integration_agent/README.md b/contributing/samples/application_integration_agent/README.md
index 0e0a70c17c..961a65eb53 100644
--- a/contributing/samples/application_integration_agent/README.md
+++ b/contributing/samples/application_integration_agent/README.md
@@ -7,7 +7,7 @@ This sample demonstrates how to use the `ApplicationIntegrationToolset` within a
 ## Prerequisites
 
 1.  **Set up Integration Connection:**
-    *   You need an existing [Integration connection](https://cloud.google.com/integration-connectors/docs/overview) configured to interact with your Jira instance. Follow the [documentation](https://google.github.io/adk-docs/tools/google-cloud-tools/#use-integration-connectors) to provision the Integration Connector in Google Cloud and then use this [documentation](https://cloud.google.com/integration-connectors/docs/connectors/jiracloud/configure) to create an Jira connection. Note the `Connection Name`, `Project ID`, and `Location` of your connection.
+    *   You need an existing [Integration connection](https://cloud.google.com/integration-connectors/docs/overview) configured to interact with your Jira instance. Follow the [documentation](https://google.github.io/adk-docs/tools/google-cloud-tools/#use-integration-connectors) to provision the Integration Connector in Google Cloud and then use this [documentation](https://cloud.google.com/integration-connectors/docs/connectors/jiracloud/configure) to create a Jira connection. Note the `Connection Name`, `Project ID`, and `Location` of your connection.
     * 
 
 2.  **Configure Environment Variables:**
diff --git a/contributing/samples/multi_agent_seq_config/README.md b/contributing/samples/multi_agent_seq_config/README.md
index a2cd462465..af0dcee2fc 100644
--- a/contributing/samples/multi_agent_seq_config/README.md
+++ b/contributing/samples/multi_agent_seq_config/README.md
@@ -6,7 +6,7 @@ The whole process is:
 
 1. An agent backed by a cheap and fast model to write initial version.
 2. An agent backed by a smarter and a little more expensive to review the code.
-3. An final agent backed by the smartest and slowest model to write the final revision.
+3. A final agent backed by the smartest and slowest model to write the final revision.
 
 Sample queries:
 
diff --git a/contributing/samples/spanner_rag_agent/README.md b/contributing/samples/spanner_rag_agent/README.md
index 99b60794fe..08d134b990 100644
--- a/contributing/samples/spanner_rag_agent/README.md
+++ b/contributing/samples/spanner_rag_agent/README.md
@@ -181,7 +181,7 @@ type.
 
 ## 💬 Sample prompts
 
-* I'd like to buy a starter bike for my 3 year old child, can you show me the recommendation?
+* I'd like to buy a starter bike for my 3-year-old child, can you show me the recommendation?
 
 ![Spanner RAG Sample Agent](Spanner_RAG_Sample_Agent.png)
 
diff --git a/src/google/adk/tools/pubsub/__init__.py b/src/google/adk/tools/pubsub/__init__.py
index aae36ae528..d488c317d9 100644
--- a/src/google/adk/tools/pubsub/__init__.py
+++ b/src/google/adk/tools/pubsub/__init__.py
@@ -14,7 +14,7 @@
 
 """Pub/Sub Tools (Experimental).
 
-Pub/Sub Tools under this module are hand crafted and customized while the tools
+Pub/Sub Tools under this module are handcrafted and customized while the tools
 under google.adk.tools.google_api_tool are auto generated based on API
 definition. The rationales to have customized tool are:
 
diff --git a/src/google/adk/tools/spanner/settings.py b/src/google/adk/tools/spanner/settings.py
index dca8ef098b..ca7e05f1b1 100644
--- a/src/google/adk/tools/spanner/settings.py
+++ b/src/google/adk/tools/spanner/settings.py
@@ -115,7 +115,7 @@ class VectorSearchIndexSettings(BaseModel):
   """
 
   num_branches: Optional[int] = None
-  """Optional. The number of branches to further parititon the vector data.
+  """Optional. The number of branches to further partition the vector data.
 
   You can only designate num_branches for trees with 3 levels.
   The number of branches must be fewer than the number of leaves
@@ -165,7 +165,7 @@ class SpannerVectorStoreSettings(BaseModel):
   """Required. The vector store table columns to return in the vector similarity search result.
 
   By default, only the `content_column` value and the distance value are returned.
-  If sepecified, the list of selected columns and the distance value are returned.
+  If specified, the list of selected columns and the distance value are returned.
   For example, if `selected_columns` is ['col1', 'col2'], then the result will contain the values of 'col1' and 'col2' columns and the distance value.
   """