MCP endpoint in Agent Server ↗

pip install "langgraph-api>=0.2.3" "langgraph-sdk>=0.1.61"

uv add "langgraph-api>=0.2.3" "langgraph-sdk>=0.1.61"

    npm install @modelcontextprotocol/sdk
    ```

> **Note**
> Replace `serverUrl` with your Agent Server URL and configure authentication headers as needed.

```js
    import { Client } from "@modelcontextprotocol/sdk/client/index.js";
    import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp.js";

    // Connects to the LangGraph MCP endpoint
    async function connectClient(url) {
        const baseUrl = new URL(url);
        const client = new Client({
            name: 'streamable-http-client',
            version: '1.0.0'
        });

        const transport = new StreamableHTTPClientTransport(baseUrl);
        await client.connect(transport);

        console.log("Connected using Streamable HTTP transport");
        console.log(JSON.stringify(await client.listTools(), null, 2));
        return client;
    }

    const serverUrl = "http://localhost:2024/mcp";

    connectClient(serverUrl)
        .then(() => {
            console.log("Client connected successfully");
        })
        .catch(error => {
            console.error("Failed to connect client:", error);
        });
    ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="Python"></span>
Install the adapter with:

```bash
    pip install langchain-mcp-adapters
    ```

Here is an example of how to connect to a remote MCP endpoint and use an agent as a tool:

```python
    # Create server parameters for stdio connection
    from mcp import ClientSession
    from mcp.client.streamable_http import streamablehttp_client
    import asyncio

    from langchain_mcp_adapters.tools import load_mcp_tools
    from langchain.agents import create_agent


    server_params = {
        "url": "https://mcp-finance-agent.xxx.us.langgraph.app/mcp",
        "headers": {
            "X-Api-Key":"lsv2_pt_your_api_key"
        }
    }

    async def main():
        async with streamablehttp_client(**server_params) as (read, write, _):
            async with ClientSession(read, write) as session:
                # Initialize the connection
                await session.initialize()

                # Load the remote graph as if it was a tool
                tools = await load_mcp_tools(session)

                # Create and run a react agent with the tools
                agent = create_agent("gpt-4.1", tools)

                # Invoke the agent with a message
                agent_response = await agent.ainvoke({"messages": "What can the finance agent do for me?"})
                print(agent_response)

    if __name__ == "__main__":
        asyncio.run(main())
    ```
  <span class="tab-end"></span>
<span class="tab-group-end"></span>

## Expose an agent as MCP tool

When deployed, your agent will appear as a tool in the MCP endpoint
with this configuration:

* **Tool name**: The agent's name.
* **Tool description**: The agent's description.
* **Tool input schema**: The agent's input schema.

### Setting name and description

You can set the name and description of your agent in `langgraph.json`:

```json
{
    "graphs": {
        "my_agent": {
            "path": "./my_agent/agent.py:graph",
            "description": "A description of what the agent does"
        }
    },
    "env": ".env"
}

from langgraph.graph import StateGraph, START, END
from typing_extensions import TypedDict

# Define input schema
class InputState(TypedDict):
    question: str

# Define output schema
class OutputState(TypedDict):
    answer: str

# Combine input and output
class OverallState(InputState, OutputState):
    pass

# Define the processing node
def answer_node(state: InputState):
    # Replace with actual logic and do something useful
    return {"answer": "bye", "question": state["question"]}

# Build the graph with explicit schemas
builder = StateGraph(OverallState, input_schema=InputState, output_schema=OutputState)
builder.add_node(answer_node)
builder.add_edge(START, "answer_node")
builder.add_edge("answer_node", END)
graph = builder.compile()

# Run the graph
print(graph.invoke({"question": "hi"}))

from langchain_mcp_adapters.client import MultiServerMCPClient

def mcp_tools_node(state, config):
    user = config["configurable"].get("langgraph_auth_user")
         , user["github_token"], user["email"], etc.

    client = MultiServerMCPClient({
        "github": {
            "transport": "streamable_http", # (1)
            "url": "https://my-github-mcp-server/mcp", # (2)
            "headers": {
                "Authorization": f"Bearer {user['github_token']}"
            }
        }
    })
    tools = await client.get_tools() # (3)

    # Your tool-calling logic here

    tool_messages = ...
    return {"messages": tool_messages}

{
  "$schema": "https://langgra.ph/schema.json",
  "http": {
    "disable_mcp": true
  }
}