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

# Several servers at once

> Give one agent the tools from multiple MCP servers with mcp_urls, and let it cross-reference sources in a single run.

Pass a list to `mcp_urls` and the agent loads the tools from *every* server and can use them together in a single run. The model sees the union of both toolsets and decides which to call; each call is routed back to the server that owns it.

|                     |                                                                          |
| ------------------- | ------------------------------------------------------------------------ |
| **Servers**         | `https://mcp.deepwiki.com/mcp` and `https://learn.microsoft.com/api/mcp` |
| **Auth**            | none for either                                                          |
| **Model used here** | `claude-sonnet-5`                                                        |

## Build it

<Steps>
  <Step title="Install and set your key">
    ```bash theme={null}
    pip install -U swarms
    export ANTHROPIC_API_KEY="sk-ant-..."
    ```
  </Step>

  <Step title="Pass a list instead of a string">
    ```python theme={null}
    from swarms import Agent

    agent = Agent(
        agent_name="Multi-MCP-Agent",
        agent_description="Research agent with tools from several free MCP servers.",
        model_name="claude-sonnet-5",
        mcp_urls=[
            "https://mcp.deepwiki.com/mcp",         # GitHub repo Q&A
            "https://learn.microsoft.com/api/mcp",  # Microsoft docs
        ],
        max_loops=2,
    )
    ```

    `mcp_urls` is the only change from the single-server examples. Routing, transport selection, and name collisions are handled for you.
  </Step>

  <Step title="Give it more than one loop">
    Two servers means at least two rounds of tool calls before the model can answer. `max_loops=1` will cut it off after the first — the run returns, but half the question is unanswered.
  </Step>

  <Step title="Ask something that needs both">
    ```python theme={null}
    result = agent.run(
        "First, use DeepWiki to describe what the modelcontextprotocol/"
        "python-sdk repository does. Then use Microsoft Learn to find how "
        "Azure Functions supports Python. Give one combined summary."
    )
    print(result)
    ```

    Naming the sources in the task keeps the model from trying to answer the Azure half out of the repo server.
  </Step>
</Steps>

## The complete script

```python theme={null}
from swarms import Agent

agent = Agent(
    agent_name="Multi-MCP-Agent",
    agent_description="Research agent with tools from several free MCP servers.",
    model_name="claude-sonnet-5",
    mcp_urls=[
        "https://mcp.deepwiki.com/mcp",         # GitHub repo Q&A
        "https://learn.microsoft.com/api/mcp",  # Microsoft docs
    ],
    max_loops=2,  # give the model room to call tools on both servers
)

if __name__ == "__main__":
    result = agent.run(
        "First, use DeepWiki to describe what the modelcontextprotocol/"
        "python-sdk repository does. Then use Microsoft Learn to find how "
        "Azure Functions supports Python. Give one combined summary."
    )
    print(result)
```

## Mixing authenticated and open servers

`mcp_api_key` applies to every server that does not define its own credential. When servers need *different* keys, give each one a connection object:

```python theme={null}
import os

from swarms import Agent
from swarms.schemas.mcp_schemas import MCPConnection

agent = Agent(
    agent_name="Research-Agent",
    model_name="claude-sonnet-5",
    mcp_urls=[
        "https://mcp.deepwiki.com/mcp",                                   # open
        f"https://mcp.exa.ai/mcp?exaApiKey={os.getenv('EXA_API_KEY')}",   # key in the URL
        MCPConnection(                                                     # key in a header
            url="https://mcp.semgrep.ai/mcp",
            api_key="env:SEMGREP_APP_TOKEN",
            name="semgrep",
        ),
    ],
    max_loops=3,
)
```

Strings and `MCPConnection` objects can be mixed in the same list. See [authentication patterns](/examples/mcp/authentication) for the full set of credential shapes.

## Local servers

The same list works for servers you run yourself — useful when a private tool server sits alongside a public one:

```python theme={null}
agent = Agent(
    agent_name="Quantitative-Trading-Agent",
    model_name="claude-sonnet-5",
    mcp_urls=[
        "http://localhost:8000/mcp",
        "http://localhost:8001/mcp",
    ],
    max_loops=1,
)
```

Start the servers first — see [build your own server](/examples/mcp/local-server).

## When to stop adding servers

Tools are not free. Every schema occupies context on every call, and a model choosing among forty tools picks wrong more often than one choosing among four.

| Situation                             | Do this                                                                                  |
| ------------------------------------- | ---------------------------------------------------------------------------------------- |
| Two or three servers, one job         | `mcp_urls` on a single agent — this page.                                                |
| Many tools, most irrelevant per task  | [Dynamic tool loading](/examples/mcp/dynamic-tool-loading) — defer schemas until needed. |
| Distinct stages with distinct sources | [One server per agent in a workflow](/examples/mcp/sequential-workflow).                 |

<Note>
  Source: [examples/mcp/agents/04\_multi\_server\_agent.py](https://github.com/kyegomez/swarms/blob/master/examples/mcp/agents/04_multi_server_agent.py) and [multi\_mcp\_urls.py](https://github.com/kyegomez/swarms/blob/master/examples/mcp/agents/multi_mcp_urls.py)
</Note>