MCP Support

Create, manage, and connect MCP servers easily to integrate dynamic tools and enhance your AI workflows with full tracing.

LangDB simplifies how you work with MCP (Model Context Protocol) servers — whether you want to use a built-in Virtual MCP or connect to an external MCP server.

Browse publicly-available MCP servers on LangDB

Model Context Protocol (MCP) is an open standard that enables AI models to seamlessly communicate with external systems. It allows models to dynamically process contextual data, ensuring efficient, adaptive, and scalable interactions. MCP simplifies request orchestration across distributed AI systems, enhancing interoperability and context-awareness.

With native tool integrations, MCP connects AI models to APIs, databases, local files, automation tools, and remote services through a standardized protocol. Developers can effortlessly integrate MCP with IDEs, business workflows, and cloud platforms, while retaining the flexibility to switch between LLM providers. This enables the creation of intelligent, multi-modal workflows where AI securely interacts with real-world data and tools.

For more details, visit the Model Context Protocol official page and explore Anthropic MCP documentation.

Using Virtual MCPs

Using API

LangDB allows you to create Virtual MCP Servers directly from the dashboard. You can instantly select and bundle tools like database queries, search APIs, or automation tasks into a single MCP URL — no external setup needed.

Here's an example of how you can use a Virtual MCP Server in your project:

Python

from openai import OpenAI
from uuid import uuid4

client = OpenAI(
    base_url="https://api.us-east-1.langdb.ai/LangDBProjectID/v1",
    api_key="xxxx",
    default_headers={"x-thread-id": str(uuid4())},
)
mcpServerUrl = "Virtual MCP Server URL"
response = client.chat.completions.create(
    model="openai/gpt-4.1",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user",   "content": "What are the databases available"}
    ],
    extra_body={
        "mcp_servers": [
            {
                "server_url": mcpServerUrl,
                "type": "sse"
            }
        ]
    }
)

TypeScript

import openai, {
  OpenAI
} from 'openai';
import { v4 as uuid4 } from 'uuid';

const client = new OpenAI({
  baseURL: "https://api.us-east-1.langdb.ai/LangDBProjectID/v1",
  apiKey: "xxxx",
  defaultHeaders: {
    "x-thread-id": uuid4()
  }
});
const mcpServerUrl = 'Virtual MCP URL';
async function getAssistantReply() {
  const {
    choices
  } = await client.chat.completions.create({
    model: "openai/gpt-4.1-nano",
    messages: [
    {role: "system", content: "You are a helpful assistant."},
    {role: "user", content: "what are the databases on clickhouse?"} ,
    // @ts-expect-error mcp_servers is a LangDB extension
    mcp_servers: [
      { server_url: mcpServerUrl, type: 'sse' }
    ] 
  }
);
  console.log('Assistant:', choices[0].message.content);
}

Checkout Virtual MCP and Guides section for usecases.

Using MCP Clients

You can instantly connect LangDB’s Virtual MCP servers to editors like Cursor, Claude, or Windsurf.

Run this in your terminal to set up MCP in Cursor:

npx @langdb/mcp setup <server_name> <mcp_url> --client cursor

You can now call tools directly in your editor, with full tracing on LangDB.

Connecting to External MCP Servers

If you already have an MCP server hosted externally — like Smithery’s Exa MCP — you can plug it straight into LangDB with zero extra setup.

Just pass your external MCP server URL in extra_body when you make a chat completion request. For example Smithery:

extra_body = {
    "mcp_servers": [
        {
            "server_url": "wss://your-mcp-server.com/ws?config=your_encoded_config",
            "type": "ws"
        }
    ]
}

For a complete example of how to use external MCP, refer to the Smithery guide.