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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.

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Creating an MCP Server from a Team

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.

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Using a Team MCP Server from Another Agent

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)

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Combining Multiple Teams

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.

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Transport Options

# 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)

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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 MCP Tools Overview.