CrewAI

Build a CrewAI agent that reads a user’s Gmail inbox. Scalekit handles OAuth, token storage, and exposes tools over MCP. CrewAI’s MCPServerAdapter discovers the tools automatically — no manual schema conversion needed.

Full code on GitHub

Install

pip install crewai crewai-tools scalekit-sdk-python python-dotenv

Initialize

import os
from scalekit import ScalekitClient
from dotenv import find_dotenv, load_dotenv

load_dotenv(find_dotenv())

scalekit_client = ScalekitClient(
    env_url=os.environ["SCALEKIT_ENV_URL"],
    client_id=os.environ["SCALEKIT_CLIENT_ID"],
    client_secret=os.environ["SCALEKIT_CLIENT_SECRET"],
)
actions = scalekit_client.actions

Connect the user to Gmail

response = actions.get_or_create_connected_account(
    connection_name="gmail",
    identifier="user_123",
)
if response.connected_account.status != "ACTIVE":
    link = actions.get_authorization_link(connection_name="gmail", identifier="user_123")
    print("Authorize Gmail:", link.link)
    input("Press Enter after authorizing...")

See Authorize a user for production auth handling.

Build and run the agent

Get the Virtual MCP Server URL and mint a session token, then pass both to MCPServerAdapter. CrewAI discovers all available Gmail tools from the MCP server:

from crewai import Agent, Crew, LLM, Task
from crewai_tools import MCPServerAdapter
from datetime import timedelta

# Retrieve config_id by listing Virtual MCP Servers filtered by name
list_response = actions.mcp.list_configs(filter_name="gmail-user-tools")
mcp_server_url = list_response.configs[0].mcp_server_url
mcp_id = list_response.configs[0].id

token_response = actions.mcp.create_session_token(
    mcp_config_id=mcp_id,
    identifier="user_123",
    expiry=timedelta(hours=1),
)

with MCPServerAdapter({
    "url": mcp_server_url,
    "headers": {"Authorization": f"Bearer {token_response.token}"},
    "transport": "streamable-http",
}) as tools:
    agent = Agent(
        role="Email Assistant",
        goal="Fetch and summarize the user's unread emails",
        backstory="You are a helpful assistant with access to the user's Gmail inbox.",
        tools=tools,
        llm=LLM(
            model=os.getenv("LLM_MODEL", "gpt-4o"),
            base_url=os.getenv("OPENAI_BASE_URL"),
            api_key=os.getenv("OPENAI_API_KEY"),
        ),
        verbose=True,
    )

    task = Task(
        description="Fetch the last 5 unread emails and provide a brief summary of each.",
        expected_output="A list of 5 unread emails with subject, sender, and a one-sentence summary.",
        agent=agent,
    )

    result = Crew(agents=[agent], tasks=[task]).kickoff()
    print(result)

Nullable schema fields

Some Scalekit tool schemas include nullable types ({"type": ["string", "null"]}) that CrewAI’s schema converter doesn’t handle out of the box. If you see a TypeError during tool parsing, apply the schema patch at the top of your script.

Multi-agent crew

CrewAI’s real strength is multi-agent orchestration. For a full example that splits email triage across three specialized agents (scanner, prioritizer, drafter), see the CrewAI email triage cookbook.

Get the MCP server URL

The code above reads mcp_server_url from a Virtual MCP Server config. Create a config in the Scalekit Dashboard under AgentKit → MCP Configs. See Virtual MCP Servers for setup details.