litellm MCP

docs/my-website/docs/mcp.md

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+# /mcp Model Context Protocol [BETA]
+
+## Overview
+
+LiteLLM's MCP implementation allows you to define tools that can be called by any MCP compatible client. Define your `mcp_tools` with LiteLLM and all your clients can `list` and `call` available tools. 
+
+## How it works 
+
+LiteLLM exposes the following MCP endpoints:
+
+- `/mcp/list_tools` - List all available tools
+- `/mcp/call_tool` - Call a specific tool with the provided arguments
+
+When MCP clients connect to LiteLLM they can follow this workflow:
+
+1. Connect to the LiteLLM MCP server
+2. List all available tools on LiteLLM
+3. Client makes LLM API request with tool call(s)
+4. LLM API returns which tools to call and with what arguments
+5. MCP client makes tool calls to LiteLLM
+6. LiteLLM makes the tool calls to the appropriate handlers
+7. LiteLLM returns the tool call results to the MCP client
+
+## Quick Start
+
+### 1. Define your tools on mcp_tools
+
+```yaml
+model_list:
+  - model_name: gpt-3.5-turbo-end-user-test
+    litellm_params:
+      model: azure/chatgpt-v-2
+      api_base: https://openai-gpt-4-test-v-1.openai.azure.com/
+      api_version: "2023-05-15"
+      api_key: os.environ/AZURE_API_KEY
+
+
+
+mcp_tools:
+  - name: "get_current_time"
+    description: "Get the current time"
+    input_schema: {
+      "type": "object",
+      "properties": {
+        "format": {
+          "type": "string",
+          "description": "The format of the time to return",
+          "enum": ["short"]
+        }
+      }
+    }
+    handler: "mcp_tools.get_current_time"
+```
+
+### 2. Start LiteLLM Proxy Server
+
+### 3. Make an LLM API request 
+
+
+
+```python
+import asyncio
+from langchain_mcp_adapters.tools import load_mcp_tools
+from langchain_openai import ChatOpenAI
+from langgraph.prebuilt import create_react_agent
+from mcp import ClientSession
+from mcp.client.sse import sse_client
+
+
+async def main():
+    # Initialize the model with your API key
+    model = ChatOpenAI(model="gpt-4o")
+    
+    # Connect to the MCP server
+    async with sse_client(url="http://localhost:4000/mcp/") as (read, write):
+        async with ClientSession(read, write) as session:
+            # Initialize the session
+            print("Initializing session...")
+            await session.initialize()
+            print("Session initialized")
+
+            # Load available tools from MCP
+            print("Loading tools...")
+            tools = await load_mcp_tools(session)
+            print(f"Loaded {len(tools)} tools")
+
+            # Create a ReAct agent with the model and tools
+            agent = create_react_agent(model, tools)
+            
+            # Run the agent with a user query
+            user_query = "What's the weather in Tokyo?"
+            print(f"Asking: {user_query}")
+            agent_response = await agent.ainvoke({"messages": user_query})
+            print("Agent response:")
+            print(agent_response)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
+
+```
+
+
+

docs/my-website/sidebars.js

@@ -301,6 +301,7 @@ const sidebars = {
             "image_variations",
           ]
         },
+        "mcp",
         {
           type: "category",
           label: "/audio",