docs: update mcp docs (#5191)

Update MCP docs

docs/docs/agents/mcp.md

@@ -7,7 +7,7 @@ hide:
   - tags
 ---
 
-# MCP Integration
+# Use MCP
 
 [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) is an open protocol that standardizes how applications provide tools and context to language models. LangGraph agents can use tools defined on MCP servers through the `langchain-mcp-adapters` library.
 
@@ -23,41 +23,91 @@ pip install langchain-mcp-adapters
 
 The `langchain-mcp-adapters` package enables agents to use tools defined across one or more MCP servers.
 
-```python title="Agent using tools defined on MCP servers"
-# highlight-next-line
-from langchain_mcp_adapters.client import MultiServerMCPClient
-from langgraph.prebuilt import create_react_agent
 
-# highlight-next-line
-client = MultiServerMCPClient(
-    {
-        "math": {
-            "command": "python",
-            # Replace with absolute path to your math_server.py file
-            "args": ["/path/to/math_server.py"],
-            "transport": "stdio",
-        },
-        "weather": {
-            # Ensure you start your weather server on port 8000
-            "url": "http://localhost:8000/mcp",
-            "transport": "streamable_http",
-        }
-    }
-)
-# highlight-next-line
-tools = await client.get_tools()
-agent = create_react_agent(
-    "anthropic:claude-3-7-sonnet-latest",
+=== "In an agent"
+
+    ```python title="Agent using tools defined on MCP servers"
     # highlight-next-line
-    tools
-)
-math_response = await agent.ainvoke(
-    {"messages": [{"role": "user", "content": "what's (3 + 5) x 12?"}]}
-)
-weather_response = await agent.ainvoke(
-    {"messages": [{"role": "user", "content": "what is the weather in nyc?"}]}
-)
-```
+    from langchain_mcp_adapters.client import MultiServerMCPClient
+    from langgraph.prebuilt import create_react_agent
+
+    # highlight-next-line
+    client = MultiServerMCPClient(
+        {
+            "math": {
+                "command": "python",
+                # Replace with absolute path to your math_server.py file
+                "args": ["/path/to/math_server.py"],
+                "transport": "stdio",
+            },
+            "weather": {
+                # Ensure you start your weather server on port 8000
+                "url": "http://localhost:8000/mcp",
+                "transport": "streamable_http",
+            }
+        }
+    )
+    # highlight-next-line
+    tools = await client.get_tools()
+    agent = create_react_agent(
+        "anthropic:claude-3-7-sonnet-latest",
+        # highlight-next-line
+        tools
+    )
+    math_response = await agent.ainvoke(
+        {"messages": [{"role": "user", "content": "what's (3 + 5) x 12?"}]}
+    )
+    weather_response = await agent.ainvoke(
+        {"messages": [{"role": "user", "content": "what is the weather in nyc?"}]}
+    )
+    ```
+
+=== "In a workflow"
+
+    ```python
+    from langchain_mcp_adapters.client import MultiServerMCPClient
+    from langgraph.graph import StateGraph, MessagesState, START
+    from langgraph.prebuilt import ToolNode, tools_condition
+
+    from langchain.chat_models import init_chat_model
+    model = init_chat_model("openai:gpt-4.1")
+
+    client = MultiServerMCPClient(
+        {
+            "math": {
+                "command": "python",
+                # Make sure to update to the full absolute path to your math_server.py file
+                "args": ["./examples/math_server.py"],
+                "transport": "stdio",
+            },
+            "weather": {
+                # make sure you start your weather server on port 8000
+                "url": "http://localhost:8000/mcp/",
+                "transport": "streamable_http",
+            }
+        }
+    )
+    tools = await client.get_tools()
+
+    def call_model(state: MessagesState):
+        response = model.bind_tools(tools).invoke(state["messages"])
+        return {"messages": response}
+
+    builder = StateGraph(MessagesState)
+    builder.add_node(call_model)
+    builder.add_node(ToolNode(tools))
+    builder.add_edge(START, "call_model")
+    builder.add_conditional_edges(
+        "call_model",
+        tools_condition,
+    )
+    builder.add_edge("tools", "call_model")
+    graph = builder.compile()
+    math_response = await graph.ainvoke({"messages": "what's (3 + 5) x 12?"})
+    weather_response = await graph.ainvoke({"messages": "what is the weather in nyc?"})
+    ```
+
+
 
 ## Custom MCP servers
 
@@ -106,4 +156,5 @@ if __name__ == "__main__":
 ## Additional resources
 
 - [MCP documentation](https://modelcontextprotocol.io/introduction)
-- [MCP Transport documentation](https://modelcontextprotocol.io/docs/concepts/transports)
+- [MCP Transport documentation](https://modelcontextprotocol.io/docs/concepts/transports)
+- [langchain_mcp_adapters](https://github.com/langchain-ai/langchain-mcp-adapters)

docs/docs/concepts/server-mcp.md

@@ -6,20 +6,14 @@ hide:
   - tags
 ---
 
-# MCP Endpoint
+# MCP endpoint in LangGraph Server
 
 The **Model Context Protocol (MCP)** is an open protocol for describing tools and data sources in a model-agnostic format, enabling LLMs to discover
 and use them via a structured API. 
 
 [LangGraph Server](./langgraph_server.md) implements MCP using the [Streamable HTTP transport](https://spec.modelcontextprotocol.io/specification/2025-03-26/basic/transports/#streamable-http). This allows LangGraph **agents** to be exposed as **MCP tools**, making them usable with any MCP-compliant client supporting Streamable HTTP.
 
-The MCP endpoint is available at:
-
-```
-/mcp
-```
-
-on [LangGraph Server](./langgraph_server.md).
+The MCP endpoint is available at `/mcp` on [LangGraph Server](./langgraph_server.md).
 
 ## Requirements
 

docs/mkdocs.yml

@@ -172,8 +172,8 @@ nav:
       - Prebuilt implementation: agents/multi-agent.md
       - Custom implementation: how-tos/multi_agent.ipynb
     - MCP:
-      - Use MCP tools: agents/mcp.md
-      - Server deployment via MCP: concepts/server-mcp.md
+      - Use MCP: agents/mcp.md
+      - Server API: concepts/server-mcp.md
     - Deployment:
       - Basic deployment: agents/deployment.md
       - Set up your application: