Several servers at once

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

Install and set your key

pip install -U swarms
export ANTHROPIC_API_KEY="sk-ant-..."

Pass a list instead of a string

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 is handled for you: each call goes back to the server that advertised the tool. If two servers expose a tool with the same name, the first server’s tool is kept and a warning is logged.

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.

Ask something that needs both

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.

The complete script

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:

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 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:

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.

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 — defer schemas until needed.
Distinct stages with distinct sources	One server per agent in a workflow.

Source: examples/mcp/agents/04_multi_server_agent.py and multi_mcp_urls.py

Index

Basic Examples

Model Providers

Single Agent

Tree of Thoughts

Multi-Agent Examples

Auto Agent Builder

Scheduled Agents

Applications

Research

Technical Analysis

Use Cases

Finance

Voice Agents

Integrations

MCP Tool Integrations

Deployment

CLI

Several servers at once

Build it

The complete script

Mixing authenticated and open servers

Local servers

When to stop adding servers

​Build it

​The complete script

​Mixing authenticated and open servers

​Local servers

​When to stop adding servers

Build it

The complete script

Mixing authenticated and open servers

Local servers

When to stop adding servers