building-agent-mcp-server

name	building-agent-mcp-server
description	Guides developers through creating a Scalekit MCP server with authenticated tool access. Use when building an MCP server, exposing Scalekit tools over MCP, or connecting AI agents via LangChain/LangGraph MCP adapters.

Building an Agent MCP Server

Scalekit lets you build MCP servers that manage authentication, create personalized access URLs for users, and define which tools are accessible. You can also bundle several toolkits (e.g., Gmail + Google Calendar) within a single server.

Model Context Protocol (MCP) is an open-source standard that enables AI systems to interface with external tools and data sources. Where the integrating-agent-auth skill uses the SDK directly, this workflow exposes Scalekit tools over the MCP protocol so any compliant client — LangChain, Claude Desktop, MCP Inspector — can consume them.

Note: Agent Auth MCP servers only support Streamable HTTP transport.

What you'll build

1. A Scalekit MCP server that fetches the user's latest email and creates a reminder calendar event
2. A LangGraph agent that connects to this server via langchain-mcp-adapters and invokes the tools

Prerequisites

• Scalekit credentials: app.scalekit.com → Settings → Copy SCALEKIT_CLIENT_ID, SCALEKIT_CLIENT_SECRET, SCALEKIT_ENV_URL
• OpenAI API key: OPENAI_API_KEY

Gmail is the only connector that does not require dashboard setup. All other connectors (including Google Calendar) must be created in the Scalekit Dashboard before use:

Go to Scalekit Dashboard → Agent Auth → Connections → + Create Connection → Select connector → Set Connection Name → Save

Important: The Connection Name you set in the dashboard is exactly what you use as the connection_name parameter in your code. They must match exactly.

For this example, create the Google Calendar connector:

• Google Calendar connector: Scalekit Dashboard → Agent Auth → Connections → Create Connection → Google Calendar → Connection Name = MY_CALENDAR → Save

Step 1 — Set up your environment

Install dependencies:

pip install scalekit-sdk-python langgraph>=0.6.5 langchain-mcp-adapters>=0.1.9 python-dotenv>=1.0.1 openai>=1.53.0 requests>=2.32.3

Add these imports to main.py:

import os
import asyncio
from dotenv import load_dotenv
import scalekit.client
from scalekit.actions.models.mcp_config import McpConfigConnectionToolMapping
from scalekit.actions.types import GetMcpInstanceAuthStateResponse
from langgraph.prebuilt import create_react_agent
from langchain_mcp_adapters.client import MultiServerMCPClient

Set the OpenAI key in your environment:

export OPENAI_API_KEY=xxxxxx

Initialize the Scalekit client:

load_dotenv()

scalekit = scalekit.client.ScalekitClient(
    client_id=os.getenv("SCALEKIT_CLIENT_ID"),
    client_secret=os.getenv("SCALEKIT_CLIENT_SECRET"),
    env_url=os.getenv("SCALEKIT_ENV_URL"),
)
my_mcp = scalekit.actions.mcp

Step 2 — Create an MCP config and server instance

Define the MCP config with connection_tool_mappings — each entry maps a connector to the tools it exposes:

cfg_response = my_mcp.create_config(
    name="reminder-manager",
    description="Summarizes latest email and creates a reminder event",
    connection_tool_mappings=[
        # Gmail works directly — no dashboard setup required
        McpConfigConnectionToolMapping(
            connection_name="gmail",
            tools=[
                "gmail_fetch_mails",
            ],
        ),
        # Google Calendar must be created in dashboard first
        McpConfigConnectionToolMapping(
            connection_name="MY_CALENDAR",
            tools=[
                "googlecalendar_create_event",
            ],
        ),
    ],
)
config_name = cfg_response.config.name

Create a server instance for a specific user (john-doe). Each user gets their own instance URL:

inst_response = my_mcp.ensure_instance(
    config_name=config_name,
    user_identifier="john-doe",
)
mcp_url = inst_response.instance.url
print("Instance URL:", mcp_url)

Step 3 — Authenticate the user

Retrieve auth state and print any OAuth links the user needs to visit:

auth_state_response = my_mcp.get_instance_auth_state(
    instance_id=inst_response.instance.id,
    include_auth_links=True,
)
for conn in getattr(auth_state_response, "connections", []):
    print(
        "Connection:", conn.connection_name,
        " Provider:", conn.provider,
        " Auth Link:", conn.authentication_link,
        " Status:", conn.connected_account_status,
    )

Note: Open every printed auth link in a browser and complete OAuth before proceeding to Step 4.

Step 4 — Connect and invoke via MCP

Use MultiServerMCPClient with streamable_http transport, load the tools, and run the agent:

async def main():
    client = MultiServerMCPClient(
        {
            "reminder_demo": {
                "transport": "streamable_http",
                "url": mcp_url,
            },
        }
    )
    tools = await client.get_tools()
    agent = create_react_agent("openai:gpt-4.1", tools)
    response = await agent.ainvoke(
        {"messages": "get 1 latest email and create a calendar reminder event in next 15 mins for a duration of 15 mins."}
    )
    print(response)

asyncio.run(main())

Note — MCP client compatibility: You can test this MCP server with popular clients like MCP Inspector, Claude Desktop, and other spec-compliant implementations. Note that ChatGPT's beta connector feature may not work properly as it's still in beta and doesn't fully adhere to the MCP specification yet.

Full working example: github.com/scalekit-inc/python-connect-demos/tree/main/mcp