> **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 <auth-type>@scalekit-auth-stack`
> - **GitHub Copilot CLI**: `copilot plugin marketplace add scalekit-inc/github-copilot-authstack` then `copilot plugin install <auth-type>@scalekit-auth-stack`
> - **Codex**: run the bash installer, restart, then open Plugin Directory and enable `<auth-type>`
> - **Skills CLI** (Windsurf, Cline, 40+ agents): `npx skills add scalekit-inc/skills --list` then `--skill <skill-name>`
>
> `<auth-type>` / `<skill-name>`: `agentkit`, `full-stack-auth`, `mcp-auth`, `modular-sso`, `modular-scim` — [Full setup guide](https://docs.scalekit.com/dev-kit/build-with-ai/)

---

# Connect an MCP client

The MCP URL you generated is a standard Streamable HTTP MCP endpoint. Any spec-compliant MCP client can connect to it. No additional auth configuration, no SDK calls, no tool schema definitions are required. The client discovers available tools automatically.

## LangChain / LangGraph

```python
import asyncio
from langgraph.prebuilt import create_react_agent
from langchain_mcp_adapters.client import MultiServerMCPClient

async def run_agent(mcp_url: str):
    client = MultiServerMCPClient(
        {
            "scalekit": {
                "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 my latest email and create a calendar reminder in the next 15 minutes."
    })
    print(response)

asyncio.run(run_agent(mcp_url))
```

Install dependencies:

```sh
pip install langgraph>=0.6.5 langchain-mcp-adapters>=0.1.9 openai>=1.53.0
```

## Claude Desktop

Add the MCP URL to your Claude Desktop config (`~/Library/Application Support/Claude/claude_desktop_config.json` on macOS):

```json
{
  "mcpServers": {
    "scalekit": {
      "transport": "streamable-http",
      "url": "https://your-mcp-url-here"
    }
  }
}
```

Restart Claude Desktop. The tools appear automatically in the tool menu.

## MCP Inspector

Paste the URL directly into [MCP Inspector](https://github.com/modelcontextprotocol/inspector) to explore available tools and make live test calls before wiring up a full agent.

## Any other MCP client

The URL works with any client that implements the MCP specification using Streamable HTTP transport. Pass the URL, select the transport type, and connect. No credentials to configure.
**ChatGPT beta connector:** ChatGPT's MCP connector is still in beta and does not fully implement the MCP specification. It may not work correctly with Scalekit MCP URLs.

For full end-to-end agent examples with LangChain, Google ADK, and other frameworks, see the [Examples](/agentkit/examples/langchain/) section.

Full working code is on [GitHub](https://github.com/scalekit-inc/python-connect-demos/tree/main/mcp).

---

## 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 |