docs: add /mcp endpoint concept for LangGraph Server (#4151)

Documents the /mcp endpoint for LangGraph Server
2026-09-28 12:35:08 +02:00 · 2025-05-01 22:14:22 -04:00
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@@ -75,6 +75,7 @@ The LangGraph Platform comprises several components that work together to suppor
 - [Cron Jobs](./langgraph_server.md#cron-jobs): Cron jobs are a way to schedule tasks to run at specific times in your LangGraph application.
 - [Double Texting](./double_texting.md): Double texting is a common issue in LLM applications where users may send multiple messages before the graph has finished running. This guide explains how to handle double texting with LangGraph Deploy.
 - [Authentication & Access Control](./auth.md): Learn about options for authentication and access control when deploying the LangGraph Platform.
+- [MCP Endpoint](./server-mcp.md): Expose your LangGraph agents as MCP tools using an MCP endpoint.

 ### Deployment Options

@@ -0,0 +1,188 @@
+---
+tags:
+  - mcp
+  - platform
+hide:
+  - tags
+---
+
+# MCP Endpoint
+
+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).
+
+## Requirements
+
+To use MCP, ensure you have the following dependencies installed:
+
+- `langgraph-api >= 0.2.3`
+- `langgraph-sdk >= 0.1.61`
+
+Install them with:
+
+```bash
+pip install "langgraph-api>=0.2.3" "langgraph-sdk>=0.1.61"
+```
+
+## Exposing 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"
+}
+```
+
+After deployment, you can update the name and description using the LangGraph SDK.
+
+### Schema
+
+Define clear, minimal input and output schemas to avoid exposing unnecessary internal complexity to the LLM.
+
+The default [MessagesState](./low_level.md#messagesstate) uses `AnyMessage`, which supports many message types but is too general for direct LLM exposure.
+
+Instead, define **custom agents or workflows** that use explicitly typed input and output structures.
+
+For example, a workflow answering documentation questions might look like this:
+
+```python
+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=InputState, output=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"}))
+```
+
+For more details, see the [low-level concepts guide](https://langchain-ai.github.io/langgraph/concepts/low_level/#state).
+
+
+## Usage overview
+
+To enable MCP:
+
+- Upgrade to use langgraph-api>=0.2.3. If you are deploying LangGraph Platform, this will be done for you automatically if you create a new revision.
+- MCP tools (agents) will be automatically exposed.
+- Connect with any MCP-compliant client that supports Streamable HTTP.
+
+
+### Client
+
+Use an MCP-compliant client to connect to the LangGraph server. The following examples show how to connect using different programming languages.
+
+=== "JavaScript/TypeScript"
+
+    ```bash
+    npm install @modelcontextprotocol/sdk
+    ```
+
+    > **Note**
+    > Replace `serverUrl` with your LangGraph 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);
+        });
+    ```
+
+=== "Python"
+
+    No official MCP client is available for Python yet.
+
+
+## Session behavior  
+
+The current LangGraph MCP implementation does not support sessions. Each `/mcp` request is stateless and independent.
+
+## Authentication
+
+The `/mcp` endpoint uses the same authentication as the rest of the LangGraph API. Refer to the [authentication guide](./auth.md) for setup details.
+
+## Disabling MCP
+
+To disable the MCP endpoint, set `disable_mcp` to `true` in your `langgraph.json` configuration file:
+
+```json
+{
+  "http": {
+    "disable_mcp": true
+  }
+}
+```
+
+This will prevent the server from exposing the `/mcp` endpoint.
@@ -289,6 +289,7 @@ nav:
            - concepts/assistants.md
            - concepts/double_texting.md
            - concepts/auth.md
+            - concepts/server-mcp.md
          - Deployment Options:
            - concepts/langgraph_cloud.md
            - concepts/langgraph_self_hosted_data_plane.md