diff --git a/docs/my-website/docs/mcp.md b/docs/my-website/docs/mcp.md
new file mode 100644
index 00000000000..ef198de9478
--- /dev/null
+++ b/docs/my-website/docs/mcp.md
@@ -0,0 +1,114 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# /mcp Model Context Protocol [Beta]
+
+Use Model Context Protocol with LiteLLM. 
+
+## Overview
+
+LiteLLM supports Model Context Protocol (MCP) tools by offering a client that exposes a tools method for retrieving tools from a MCP server
+
+## Usage
+
+<Tabs>
+<TabItem value="sdk" label="LiteLLM Python SDK">
+
+```python
+import asyncio
+from litellm import experimental_create_mcp_client, completion
+from litellm.mcp_stdio import experimental_stdio_mcp_transport
+
+async def main():
+    client_one = None
+
+    try:
+        # Initialize an MCP client to connect to a `stdio` MCP server:
+        transport = experimental_stdio_mcp_transport(
+            command='node',
+            args=['src/stdio/dist/server.js']
+        )
+        client_one = await experimental_create_mcp_client(
+            transport=transport
+        )
+
+        tools = await client_one.list_tools(format="openai")
+        response = await litellm.completion(
+            model="gpt-4o",
+            tools=tools,
+            messages=[
+                {
+                    "role": "user",
+                    "content": "Find products under $100"
+                }
+            ]
+        )
+
+        print(response.text)
+    except Exception as error:
+        print(error)
+    finally:
+        await asyncio.gather(
+            client_one.close() if client_one else asyncio.sleep(0),
+        )
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+</TabItem>
+<TabItem value="proxy" label="LiteLLM Proxy Server">
+
+```python
+import asyncio
+from openai import OpenAI
+from litellm import experimental_create_mcp_client
+from litellm.mcp_stdio import experimental_stdio_mcp_transport
+
+async def main():
+    client_one = None
+
+    try:
+        # Initialize an MCP client to connect to a `stdio` MCP server:
+        transport = experimental_stdio_mcp_transport(
+            command='node',
+            args=['src/stdio/dist/server.js']
+        )
+        client_one = await experimental_create_mcp_client(
+            transport=transport
+        )
+
+        # Get tools from MCP client
+        tools = await client_one.list_tools(format="openai")
+        
+        # Use OpenAI client connected to LiteLLM Proxy Server
+        client = openai.OpenAI(
+            api_key="sk-1234",
+            base_url="http://0.0.0.0:4000"
+        )
+        response = client.chat.completions.create(
+            model="gpt-4",
+            tools=tools,
+            messages=[
+                {
+                    "role": "user",
+                    "content": "Find products under $100"
+                }
+            ]
+        )
+
+        print(response.choices[0].message.content)
+    except Exception as error:
+        print(error)
+    finally:
+        await asyncio.gather(
+            client_one.close() if client_one else asyncio.sleep(0),
+        )
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+</TabItem>
+</Tabs>
+
diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js
index 1df988f372d..a1499cc0b01 100644
--- a/docs/my-website/sidebars.js
+++ b/docs/my-website/sidebars.js
@@ -293,6 +293,7 @@ const sidebars = {
         "text_completion",
         "embedding/supported_embedding",
         "anthropic_unified",
+        "mcp",
         {
           type: "category",
           label: "/images",