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For Developer

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

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Prerequisites

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1. Create an Asana MCP Server

Use the following endpoint to create a new remote Asana 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 an Asana MCP server instance
asana_server = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.ASANA,
    user_id="<YOUR_USER_ID>",
    platform_name="<YOUR_PLATFORM_NAME>",
)

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Response

{
  "serverUrl": "https://asana-mcp-server.klavis.ai/mcp/?instance_id=<instance-id>",
  "instanceId": "<instance-id>",
  "oauthUrl": "https://api.klavis.ai/oauth/asana/authorize?instance_id=<instance-id>"
}
serverUrl specifies the endpoint of the Asana 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 Asana information.

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2. Implement OAuth Authorization

Redirect users to the OAuth authorization flow: 
import webbrowser

webbrowser.open(asana_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.