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

# Expose Team as MCP Server

> Turn any Team into an MCP server so other agents or MCP clients can use it as a tool

## Overview

Any Upsonic Team can be exposed as an MCP server using `as_mcp()`. The entire multi-agent workflow becomes a single `do` tool that MCP clients can call.

## Creating an MCP Server from a Team

```python theme={null}
from upsonic import Agent, Team

researcher = Agent(
    model="anthropic/claude-sonnet-4-5",
    name="Researcher",
    role="Research specialist",
    goal="Find accurate information",
)

writer = Agent(
    model="anthropic/claude-sonnet-4-5",
    name="Writer",
    role="Technical writer",
    goal="Write clear and concise text",
)

team = Team(
    entities=[researcher, writer],
    name="Research Team",
    role="Research and writing",
    goal="Produce well-written summaries",
    mode="sequential",
)

team.as_mcp().run()
```

This starts an MCP server that exposes a `do` tool. When called, it runs the full team workflow — task assignment, context sharing, and result combining — and returns the final output.

## Using a Team MCP Server from Another Agent

```python theme={null}
from upsonic import Agent, Task
from upsonic.tools.mcp import MCPHandler

research_team = MCPHandler(command="python research_team_server.py")

manager = Agent(
    model="anthropic/claude-sonnet-4-5",
    name="Manager",
)

task = Task(
    description="Use the do tool to ask: Write a summary about quantum computing breakthroughs.",
    tools=[research_team],
)

result = manager.do(task)
print(result)
```

## Combining Multiple Teams

```python theme={null}
from upsonic import Agent, Task
from upsonic.tools.mcp import MCPHandler

research = MCPHandler(command="python research_team.py", tool_name_prefix="research")
engineering = MCPHandler(command="python eng_team.py", tool_name_prefix="eng")

director = Agent(
    model="anthropic/claude-sonnet-4-5",
    name="Director",
    tools=[research, engineering],
)

result = director.do("Research cloud cost optimization and write an implementation plan.")
```

The director sees `research_do` and `eng_do` and delegates to the appropriate team.

## Transport Options

```python theme={null}
# stdio (default)
team.as_mcp().run()

# SSE
team.as_mcp().run(transport="sse", port=8000)

# Streamable HTTP
team.as_mcp().run(transport="streamable-http", port=8000)
```

## How It Works

1. `as_mcp()` creates a `FastMCP` server named after the team.
2. It registers a `do` tool whose description includes the team's `role`, `goal`, member names, and `mode`.
3. When a client calls `do(task="...")`, the team runs its full multi-agent workflow internally and returns the combined result as text.
4. `.run()` starts the server and blocks, waiting for client connections.

For more information on MCP tools, see [MCPHandler](/concepts/tools/mcp-tools/mcp-handler).