docs mcp docs update

docs/my-website/docs/mcp.md

@@ -21,13 +21,22 @@ Use Model Context Protocol with LiteLLM
 LiteLLM acts as a MCP bridge to utilize MCP tools with all LiteLLM supported models. LiteLLM offers the following features for using MCP
 
 - **List** Available MCP Tools: OpenAI clients can view all available MCP tools
+  - `litellm.experimental_mcp_client.load_mcp_tools` to list all available MCP tools
 - **Call** MCP Tools: OpenAI clients can call MCP tools
+  - `litellm.experimental_mcp_client.call_openai_tool` to call an OpenAI tool on an MCP server
 
 
 ## Usage
 
 ### 1. List Available MCP Tools
 
+In this example we'll use `litellm.experimental_mcp_client.load_mcp_tools` to list all available MCP tools on any MCP server. This method can be used in two ways:
+
+- `format="mcp"` - (default) Return MCP tools 
+  - Returns: `mcp.types.Tool`
+- `format="openai"` - Return MCP tools converted to OpenAI API compatible tools. Allows using with OpenAI endpoints.
+  - Returns: `openai.types.chat.ChatCompletionToolParam`
+
 <Tabs>
 <TabItem value="sdk" label="LiteLLM Python SDK">
 
@@ -36,12 +45,8 @@ LiteLLM acts as a MCP bridge to utilize MCP tools with all LiteLLM supported mod
 from mcp import ClientSession, StdioServerParameters
 from mcp.client.stdio import stdio_client
 import os
-from litellm.mcp_client.tools import (
-    load_mcp_tools,
-    transform_openai_tool_to_mcp_tool,
-    call_openai_tool,
-)
 import litellm
+from litellm import experimental_mcp_client
 
 
 server_params = StdioServerParameters(
@@ -56,12 +61,10 @@ async with stdio_client(server_params) as (read, write):
         await session.initialize()
 
         # Get tools
-        tools = await load_mcp_tools(session=session, format="openai")
+        tools = await experimental_mcp_client.load_mcp_tools(session=session, format="openai")
         print("MCP TOOLS: ", tools)
 
-        # Create and run the agent
         messages = [{"role": "user", "content": "what's (3 + 5)"}]
-        print(os.getenv("OPENAI_API_KEY"))
         llm_response = await litellm.acompletion(
             model="gpt-4o",
             api_key=os.getenv("OPENAI_API_KEY"),

litellm/experimental_mcp_client/__init__.py

@@ -0,0 +1,3 @@
+from .tools import call_openai_tool, load_mcp_tools
+
+__all__ = ["load_mcp_tools", "call_openai_tool"]

litellm/experimental_mcp_client/tools.py

@@ -53,7 +53,7 @@ async def load_mcp_tools(
         format: The format to convert the tools to
     By default, the tools are returned in MCP format.
 
-    If format is set to "openai", the tools are converted to OpenAI tools.
+    If format is set to "openai", the tools are converted to OpenAI API compatible tools.
     """
     tools = await session.list_tools()
     if format == "openai":
@@ -80,7 +80,15 @@ async def call_openai_tool(
     session: ClientSession,
     openai_tool: ChatCompletionToolParam,
 ) -> CallToolResult:
-    """Call an OpenAI tool using MCP client."""
+    """
+    Call an OpenAI tool using MCP client.
+
+    Args:
+        session: The MCP session to use
+        openai_tool: The OpenAI tool to call. You can get this from the `choices[0].message.tool_calls[0]` of the response from the OpenAI API.
+    Returns:
+        The result of the MCP tool call.
+    """
     mcp_tool = transform_openai_tool_to_mcp_tool(
         openai_tool=openai_tool,
     )

tests/mcp_tests/test_mcp_litellm_client.py

@@ -10,11 +10,7 @@ sys.path.insert(
 from mcp import ClientSession, StdioServerParameters
 from mcp.client.stdio import stdio_client
 import os
-from litellm.mcp_client.tools import (
-    load_mcp_tools,
-    transform_openai_tool_to_mcp_tool,
-    call_openai_tool,
-)
+from litellm import experimental_mcp_client
 import litellm
 import pytest
 import json
@@ -34,12 +30,13 @@ async def test_mcp_agent():
             await session.initialize()
 
             # Get tools
-            tools = await load_mcp_tools(session=session, format="openai")
+            tools = await experimental_mcp_client.load_mcp_tools(
+                session=session, format="openai"
+            )
             print("MCP TOOLS: ", tools)
 
             # Create and run the agent
             messages = [{"role": "user", "content": "what's (3 + 5)"}]
-            print(os.getenv("OPENAI_API_KEY"))
             llm_response = await litellm.acompletion(
                 model="gpt-4o",
                 api_key=os.getenv("OPENAI_API_KEY"),
@@ -59,7 +56,7 @@ async def test_mcp_agent():
             openai_tool = llm_response["choices"][0]["message"]["tool_calls"][0]
 
             # Call the tool using MCP client
-            call_result = await call_openai_tool(
+            call_result = await experimental_mcp_client.call_openai_tool(
                 session=session,
                 openai_tool=openai_tool,
             )