> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mcp-agent.com/llms.txt
> Use this file to discover all available pages before exploring further.

# MCP-Cloud (mcp-c)

> Deploy and host your mcp-agents and apps on the cloud.

<Info>
  `mcp-c` is in open beta, and free to use. Share feedback via [GitHub issues](https://github.com/lastmile-ai/mcp-agent/issues) or [Discord](https://lmai.link/discord/mcp-agent).
</Info>

## What is MCP-Cloud?

MCP-Cloud (mcp-c) is a fully managed cloud platform for hosting mcp-agents, apps, and mcp servers.

<iframe src="https://www.youtube.com/embed/0C4VY-3IVNU" title="mcp-agent cloud overview" width="100%" height="420" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen />

### Key Benefits

* **One runtime for any MCP application** – deploy durable `mcp-agent` workflows, FastMCP servers, or ChatGPT App backends. Everything is exposed as an MCP server at `https://<unique_id>.deployments.mcp-agent.com` ([Cloud overview](/cloud/mcp-agent-cloud/overview)).
* **Temporal-backed execution** – long-running tools and workflows run on Temporal with retries, pause/resume, and human input support ([Long-running tools](/cloud/mcp-agent-cloud/long-running-tools)).
* **Managed secrets & authentication** – manage secrets for both you, as the developer, and your users. Allow users to specify their own keys, and choose bearer or unauthenticated access today (OAuth coming soon) ([Manage secrets](/cloud/mcp-agent-cloud/manage-secrets) and [Deployment auth](/cloud/authentication/deployment-auth)).
* **Observability built in** – stream logs, forward traces, and inspect workflow history directly from the CLI ([Observability](/cloud/observability)).
* **Easy client install** – use `mcp-agent install` or `mcp-agent cloud configure` to wire the deployed server into Claude Desktop, Cursor, VS Code, or ChatGPT Apps ([Use a deployed server](/cloud/mcp-agent-cloud/use-deployed-server)).

With that context, the steps below show exactly how to deploy.

## 1. Authenticate

```bash theme={null}
uvx mcp-agent login
```

The CLI opens the Cloud dashboard so you can generate an API token. Credentials are stored under `~/.mcp-agent/`.

## 2. Deploy

From the directory containing your `mcp_agent.config.yaml` (or pass `--config-dir`):

```bash theme={null}
uvx mcp-agent deploy my-agent
```

During deployment you'll classify secrets as **deployment** (stored securely) or **user** (provided later via `mcp-agent cloud configure`).

<CodeGroup>
  ```bash basic theme={null}
  directory/
  ├── main.py
  ├── mcp_agent.config.yaml
  └── mcp_agent.secrets.yaml
  ```

  ```bash deploy theme={null}
  uvx mcp-agent deploy my-agent
  ```
</CodeGroup>

After a successful deploy you'll receive an endpoint like:

```
https://<unique_id>.deployments.mcp-agent.com
```

## 3. Connect from clients

Your cloud deployment is a standard MCP server. Use any MCP client:

<Tabs>
  <Tab title="Claude Desktop">
    ```bash theme={null}
    uvx mcp-agent install https://<unique_id>.deployments.mcp-agent.com \
      --client claude_desktop \
      --name research-buddy
    ```

    Replace `claude_desktop` with `vscode`, `cursor`, `chatgpt`, `claude_code` to install in those clients instead.
  </Tab>

  <Tab title="Python">
    1. Add the cloud server to your config so the registry knows how to connect:

       ```yaml mcp_agent.config.yaml theme={null}
       mcp:
         servers:
           my_agent_cloud:
             transport: sse
             url: "https://<unique_id>.deployments.mcp-agent.com/sse"
             headers:
               Authorization: "Bearer ${MCP_API_KEY}"
       ```

    2. Connect via the shared registry:

       ```python theme={null}
       import asyncio
       from mcp_agent.config import get_settings
       from mcp_agent.mcp.mcp_server_registry import ServerRegistry
       from mcp_agent.mcp.gen_client import gen_client

       async def use_agent():
           registry = ServerRegistry(config=get_settings())
           async with gen_client("my_agent_cloud", server_registry=registry) as client:
               result = await client.call_tool("my_tool", {"param": "value"})
               print(result)

       asyncio.run(use_agent())
       ```
  </Tab>
</Tabs>

## Monitor & manage

<Tabs>
  <Tab title="Logs">
    ```bash theme={null}
    uvx mcp-agent cloud logger tail my-agent --follow
    uvx mcp-agent cloud logger tail my-agent --grep "ERROR" --since 5m
    ```
  </Tab>

  <Tab title="Servers">
    ```bash theme={null}
    uvx mcp-agent cloud servers list
    uvx mcp-agent cloud servers describe my-agent
    ```
  </Tab>

  <Tab title="Workflows">
    ```bash theme={null}
    uvx mcp-agent cloud workflows list
    uvx mcp-agent cloud workflows describe my-agent run_123
    ```
  </Tab>
</Tabs>

## Example: 👋 Hello World agent

```python main.py theme={null}
import asyncio
from typing import Optional

from mcp_agent.app import MCPApp
from mcp_agent.agents.agent import Agent
from mcp_agent.core.context import Context as AppContext
from mcp_agent.workflows.llm.augmented_llm_openai import OpenAIAugmentedLLM

# Create the MCPApp, the root of mcp-agent.
app = MCPApp(name="hello_world", description="Hello world mcp-agent application")

# Hello world agent: an Agent using MCP servers + LLM
@app.tool()
async def finder_agent(request: str, app_ctx: Optional[AppContext] = None) -> str:
    """
    Run an Agent with access to MCP servers (fetch + filesystem) to handle 
    the input request.
    """
    agent = Agent(
        name="finder",
        instruction=(
            """You are a helpful assistant. Use MCP servers to fetch and read 
            files, then answer the request concisely."""
        ),
        server_names=["fetch", "filesystem"],
        context=app_ctx,
    )

    async with agent:
        llm = await agent.attach_llm(OpenAIAugmentedLLM)
        result = await llm.generate_str(message=request)
        return result

async def main():
    async with app.run() as agent_app:
        # Run the agent
        readme_summary = await finder_agent(
            request="Please summarize the README.md file in this directory.",
            app_ctx=agent_app.context,
        )
        print(readme_summary)


if __name__ == "__main__":
    asyncio.run(main())
```

Deploy with:

```bash theme={null}
uvx mcp-agent deploy hello-world
```

## Learn more

* [CLI reference](/reference/cli) – all commands and flags.
* [Secrets configuration](/mcp-agent-sdk/core-components/configuring-your-application#secrets) – how secrets are merged for Cloud.
* [Cloud agent server guide](/cloud/agent-server) – architecture, auth, and best practices.