> **Building with AI coding agents?** If you're using an AI coding agent, install the official Scalekit plugin. It gives your agent full awareness of the Scalekit API — reducing hallucinations and enabling faster, more accurate code generation. > > - **Claude Code**: `/plugin marketplace add scalekit-inc/claude-code-authstack` then `/plugin install @scalekit-auth-stack` > - **GitHub Copilot CLI**: `copilot plugin marketplace add scalekit-inc/github-copilot-authstack` then `copilot plugin install @scalekit-auth-stack` > - **Codex**: run the bash installer, restart, then open Plugin Directory and enable `` > - **Skills CLI** (Windsurf, Cline, 40+ agents): `npx skills add scalekit-inc/skills --list` then `--skill ` > > `` / ``: `agentkit`, `full-stack-auth`, `mcp-auth`, `modular-sso`, `modular-scim` — [Full setup guide](https://docs.scalekit.com/dev-kit/build-with-ai/) --- # Claude Managed Agents **Beta:** Claude Managed Agents is in public beta. All API requests require the `managed-agents-2026-04-01` beta header, which the Anthropic SDK sets automatically. API behavior may change between releases. Connect a Claude Managed Agent to Scalekit-authenticated Gmail tools via MCP. You generate a Scalekit MCP URL, pass it to the agent, and describe a task. Anthropic manages the agent loop: tool discovery, execution, retries, and session state. Compare this to the [Anthropic SDK example](/agentkit/examples/anthropic/): that approach uses the Messages API and requires you to fetch tool schemas, build a tool-use loop, and feed results back manually. Here, none of that exists in your code. ## Prerequisites - A Scalekit account with a Gmail connection configured. See [Configure a connection](/agentkit/connections/). - A Scalekit MCP config and a per-user instance URL already created. See [Configure an MCP server](/agentkit/mcp/configure-mcp-server/) and [Generate user MCP URLs](/agentkit/mcp/generate-user-urls/). - An [Anthropic API key](https://platform.anthropic.com/settings/keys). ## Install ```sh pip install anthropic scalekit-sdk-python ``` ## Get a Scalekit MCP URL Generate a per-user MCP URL from your existing MCP config. This URL is pre-authenticated; it encodes the user's identity and their authorized connections. ```python import os import scalekit.client scalekit_client = scalekit.client.ScalekitClient( client_id=os.getenv("SCALEKIT_CLIENT_ID"), client_secret=os.getenv("SCALEKIT_CLIENT_SECRET"), env_url=os.getenv("SCALEKIT_ENV_URL"), ) actions = scalekit_client.actions inst_response = actions.mcp.ensure_instance( config_name="email-assistant", # your MCP config name user_identifier="user_123", # your app's unique user ID ) mcp_url = inst_response.instance.url ``` **Keep the MCP URL server-side:** The MCP URL is pre-authenticated. Never expose it in client-side code or browser requests. Pass it directly to the Managed Agent from your backend. Before passing the URL to the agent, confirm the user has authorized all connections the config requires. See [Check auth state](/agentkit/mcp/generate-user-urls/#check-auth-state). ## Create the agent Define the agent once and reuse it across sessions. Pass the Scalekit MCP URL as an MCP server; no vault or additional auth configuration needed, because the URL is already authenticated. ```python from anthropic import Anthropic client = Anthropic() agent = client.beta.agents.create( name="Gmail Assistant", model="claude-opus-4-7", system="You are a helpful assistant with access to the user's Gmail account.", mcp_servers=[ { "type": "url", "name": "scalekit", "url": mcp_url, }, ], tools=[ { "type": "mcp_toolset", "mcp_server_name": "scalekit", "default_config": {"permission_policy": {"type": "always_allow"}}, }, ], ) ``` **Tool confirmation policy:** MCP tools default to an `always_ask` permission policy, which pauses the agent before each tool call. The example above sets `always_allow` on the toolset so the agent runs without interruption. See [Permission policies](https://platform.claude.com/docs/en/managed-agents/permission-policies) in the Anthropic docs. Save `agent.id`. You reference it in every session; no need to recreate the agent per user. ## Create an environment An environment is the cloud container the agent runs in. Create one and reuse it. ```python environment = client.beta.environments.create( name="gmail-agent-env", config={ "type": "cloud", "networking": {"type": "unrestricted"}, }, ) ``` ## Run a session Start a session, send a task, and stream results. No `vault_ids` needed; the Scalekit MCP URL handles authentication. ```python session = client.beta.sessions.create( agent=agent.id, environment_id=environment.id, title="Gmail session", ) with client.beta.sessions.events.stream(session.id) as stream: client.beta.sessions.events.send( session.id, events=[ { "type": "user.message", "content": [ { "type": "text", "text": "Fetch my last 5 unread emails and summarize them.", } ], }, ], ) for event in stream: match event.type: case "agent.message": for block in event.content: print(block.text, end="") case "agent.tool_use": print(f"\n[{event.name}]") case "session.status_idle": print("\n") break ``` The agent discovers available Gmail tools from the Scalekit MCP server, executes them using the user's pre-authorized credentials, and streams results back. You don't manage any of that loop.
Complete example ```python import os import scalekit.client from anthropic import Anthropic from dotenv import load_dotenv load_dotenv() # Get a pre-authenticated MCP URL for the user scalekit_client = scalekit.client.ScalekitClient( client_id=os.getenv("SCALEKIT_CLIENT_ID"), client_secret=os.getenv("SCALEKIT_CLIENT_SECRET"), env_url=os.getenv("SCALEKIT_ENV_URL"), ) actions = scalekit_client.actions inst_response = actions.mcp.ensure_instance( config_name="email-assistant", user_identifier="user_123", ) mcp_url = inst_response.instance.url # Create agent (once; reuse agent.id across sessions) client = Anthropic() agent = client.beta.agents.create( name="Gmail Assistant", model="claude-opus-4-7", system="You are a helpful assistant with access to the user's Gmail account.", mcp_servers=[ { "type": "url", "name": "scalekit", "url": mcp_url, }, ], tools=[ { "type": "mcp_toolset", "mcp_server_name": "scalekit", "default_config": {"permission_policy": {"type": "always_allow"}}, }, ], ) # Create environment (once; reuse environment.id) environment = client.beta.environments.create( name="gmail-agent-env", config={ "type": "cloud", "networking": {"type": "unrestricted"}, }, ) # Run a session session = client.beta.sessions.create( agent=agent.id, environment_id=environment.id, title="Gmail session", ) with client.beta.sessions.events.stream(session.id) as stream: client.beta.sessions.events.send( session.id, events=[ { "type": "user.message", "content": [ { "type": "text", "text": "Fetch my last 5 unread emails and summarize them.", } ], }, ], ) for event in stream: match event.type: case "agent.message": for block in event.content: print(block.text, end="") case "agent.tool_use": print(f"\n[{event.name}]") case "session.status_idle": print("\n") break ```
--- ## More Scalekit documentation | Resource | What it contains | When to use it | |----------|-----------------|----------------| | [/llms.txt](/llms.txt) | Structured index with routing hints per product area | Start here — find which documentation set covers your topic before loading full content | | [/llms-full.txt](/llms-full.txt) | Complete documentation for all Scalekit products in one file | Use when you need exhaustive context across multiple products or when the topic spans several areas | | [sitemap-0.xml](https://docs.scalekit.com/sitemap-0.xml) | Full URL list of every documentation page | Use to discover specific page URLs you can fetch for targeted, page-level answers |