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Documentation Index

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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 MCPHandler.