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No-Code

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For Developer

Follow the instructions below to integrate LinkedIn MCP server to your AI application using our API or SDK.

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Prerequisites

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1. Create a LinkedIn MCP Server

Use the following endpoint to create a new remote LinkedIn MCP server instance:

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Request

from klavis import Klavis
from klavis.types import McpServerName

klavis_client = Klavis(api_key="<YOUR_API_KEY>")

# Create a LinkedIn MCP server instance
linkedin_server = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.LINKEDIN,
    user_id="<YOUR_USER_ID>",
    platform_name="<YOUR_PLATFORM_NAME>",
)

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Response

{
  "serverUrl": "https://linkedin-mcp-server.klavis.ai/mcp/?instance_id=<instance-id>",
  "instanceId": "<instance-id>",
  "oauthUrl": "https://api.klavis.ai/oauth/linkedin/authorize?instance_id=<instance-id>"
}
serverUrl specifies the endpoint of the LinkedIn MCP server, which you can connect with the MCP client of your application.
instanceId is used for authentication and identification of your server instance. After you complete the next steps, this token allows the MCP server to access user’s private LinkedIn information.

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2. Implement OAuth Authorization

Redirect users to the OAuth authorization flow: 
import webbrowser

webbrowser.open(linkedin_server.oauth_url)
You can also specify scope and redirect_url in the authUrl, and we also support white-label. Check the API reference for more details.

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Explore MCP Server Tools

For more details about tool input schema, use the list_tool API.