diff --git a/mcp/common/src/main/java/org/springframework/ai/mcp/McpToolUtils.java b/mcp/common/src/main/java/org/springframework/ai/mcp/McpToolUtils.java
index 58400952518..2f8f366d076 100644
--- a/mcp/common/src/main/java/org/springframework/ai/mcp/McpToolUtils.java
+++ b/mcp/common/src/main/java/org/springframework/ai/mcp/McpToolUtils.java
@@ -278,7 +278,7 @@ public static McpServerFeatures.AsyncToolSpecification toAsyncToolSpecification(
 	 * </ul>
 	 * @param toolCallback the Spring AI tool callback to convert
 	 * @param mimeType the MIME type of the output content
-	 * @return an MCP asynchronous tool specificaiotn that wraps the tool callback
+	 * @return an MCP asynchronous tool specification that wraps the tool callback
 	 * @see McpServerFeatures.AsyncToolSpecification
 	 * @see Schedulers#boundedElastic()
 	 */
diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chatclient.adoc b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chatclient.adoc
index 16765d7ef6d..48f39dc70cc 100644
--- a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chatclient.adoc
+++ b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chatclient.adoc
@@ -296,7 +296,7 @@ Flux<String> output = chatClient.prompt()
 You can also stream the `ChatResponse` using the method `Flux<ChatResponse> chatResponse()`.
 
 In the future, we will offer a convenience method that will let you return a Java entity with the reactive `stream()` method.
-In the meantime, you should use the xref:api/structured-output-converter.adoc#StructuredOutputConverter[Structured Output Converter] to convert the aggregated response explicity as shown below.
+In the meantime, you should use the xref:api/structured-output-converter.adoc#StructuredOutputConverter[Structured Output Converter] to convert the aggregated response explicitly as shown below.
 This also demonstrates the use of parameters in the fluent API that will be discussed in more detail in a later section of the documentation.
 
 [source,java]
diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/embeddings/azure-openai-embeddings.adoc b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/embeddings/azure-openai-embeddings.adoc
index 034282e29d5..a66d840db7d 100644
--- a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/embeddings/azure-openai-embeddings.adoc
+++ b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/embeddings/azure-openai-embeddings.adoc
@@ -145,7 +145,7 @@ The prefix `spring.ai.azure.openai` is the property prefix to configure the conn
 | spring.ai.azure.openai.endpoint | The endpoint from the Azure AI OpenAI `Keys and Endpoint` section under `Resource Management` | -
 | spring.ai.azure.openai.openai-api-key |  (non Azure) OpenAI API key. Used to authenticate with the OpenAI service, instead of Azure OpenAI. 
 This automatically sets the endpoint to https://api.openai.com/v1. Use either `api-key` or `openai-api-key` property. 
-With this configuration the `spring.ai.azure.openai.embedding.options.deployment-name` is threated as an https://platform.openai.com/docs/models[OpenAi Model] name.| -
+With this configuration the `spring.ai.azure.openai.embedding.options.deployment-name` is treated as an https://platform.openai.com/docs/models[OpenAi Model] name.| -
 |====
 
 
diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/observability/index.adoc b/spring-ai-docs/src/main/antora/modules/ROOT/pages/observability/index.adoc
index e0341e46889..222ce3ea020 100644
--- a/spring-ai-docs/src/main/antora/modules/ROOT/pages/observability/index.adoc
+++ b/spring-ai-docs/src/main/antora/modules/ROOT/pages/observability/index.adoc
@@ -116,7 +116,7 @@ They measure the time spent in the advisor (including the time spend on the inne
 == Chat Model
 
 NOTE: Observability features are currently supported only for `ChatModel` implementations from the following AI model
-providers: Anthropic, Azure OpenAI, Mistral AI, Ollama, OpenAI, Vertex AI, MiniMax, Moonshot, QianFan, Zhiu AI.
+providers: Anthropic, Azure OpenAI, Mistral AI, Ollama, OpenAI, Vertex AI, MiniMax, Moonshot, QianFan, Zhipu AI.
 Additional AI model providers will be supported in a future release.
 
 The `gen_ai.client.operation` observations are recorded when calling the ChatModel `call` or `stream` methods.