diff --git a/notebooks/agents/mongodb_as_a_toolbox_for_llamaindex_agents.ipynb b/notebooks/agents/mongodb_as_a_toolbox_for_llamaindex_agents.ipynb
index ba5e382a..741a6194 100644
--- a/notebooks/agents/mongodb_as_a_toolbox_for_llamaindex_agents.ipynb
+++ b/notebooks/agents/mongodb_as_a_toolbox_for_llamaindex_agents.ipynb
@@ -23,7 +23,7 @@
         "**Key Features:**\n",
         "\n",
         "*   **MongoDB as a Tool Registry:** Instead of hardcoding tool definitions within the agent, this application stores tool metadata (name, description, parameters) directly in a MongoDB collection.\n",
-        "*   **MongoDB Atlas Vector Search for Tool Discovery:** LlamaIndex uses the vector embeddings of tool descriptions stored in MongoDB to perform semantic searches based on user queries. This allows the agent to dynamically discover and select the most relevant tools for a given task.\n",
+        "*   **MongoDB Vector Search for Tool Discovery:** LlamaIndex uses the vector embeddings of tool descriptions stored in MongoDB to perform semantic searches based on user queries. This allows the agent to dynamically discover and select the most relevant tools for a given task.\n",
         "*   **LlamaIndex Agent with Function Calling:** The LlamaIndex agent is configured to use the retrieved tool definitions from MongoDB to enable function calling. This means the agent can understand the user's intent and execute the appropriate Python function (tool) stored in the application.\n",
         "*   **Data Storage in MongoDB:** Besides tool definitions, the application also uses separate MongoDB collections to store operational data like customer orders, return requests, and policy documents.\n",
         "*   **Integration with External Services:** The tools defined and managed in MongoDB can interact with external services (e.g., fetching real-time data, processing requests) or perform operations on the data stored within MongoDB itself (e.g., looking up order details, creating return requests).\n",
@@ -48,7 +48,7 @@
         "- `llama-index-core`: The core LlamaIndex library.\n",
         "- `llama-index-llms-openai`: LlamaIndex integration with OpenAI LLMs.\n",
         "- `llama-index-embeddings-voyageai`: LlamaIndex integration with VoyageAI embeddings.\n",
-        "- `llama-index-vector-stores-mongodb`: LlamaIndex integration with MongoDB Atlas Vector Search.\n",
+        "- `llama-index-vector-stores-mongodb`: LlamaIndex integration with MongoDB Vector Search.\n",
         "- `llama-index-readers-file`: LlamaIndex file readers."
       ]
     },
@@ -165,9 +165,9 @@
         "id": "b6f3012b"
       },
       "source": [
-        "## Download and store policy documents into MongoDB Atlas vector store\n",
+        "## Download and store policy documents into MongoDB vector store\n",
         "\n",
-        "This cell downloads policy documents and stores them in a MongoDB Atlas vector store. It initializes a vector store, checks if the collection is empty, downloads PDF documents, loads them, adds metadata, initializes embedding and node parsing, parses documents into nodes, creates a storage context, creates a vector index, and ingests the documents."
+        "This cell downloads policy documents and stores them in a MongoDB vector store. It initializes a vector store, checks if the collection is empty, downloads PDF documents, loads them, adds metadata, initializes embedding and node parsing, parses documents into nodes, creates a storage context, creates a vector index, and ingests the documents."
       ]
     },
     {