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Overview

Any Upsonic Agent can be exposed as an MCP (Model Context Protocol) server using as_mcp(). This lets other agents, tools, or MCP-compatible clients consume your agent over stdio, SSE, or Streamable HTTP.

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Creating an MCP Server from an Agent

from upsonic import Agent

agent = Agent(
    model="anthropic/claude-sonnet-4-5",
    name="Math Expert",
    role="Mathematics specialist",
    goal="Solve math problems accurately",
)

agent.as_mcp().run()
Run this script and it starts an MCP server over stdio, exposing a do tool that accepts a task string and returns the agent’s response.

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Using an Agent MCP Server from Another Agent

from upsonic import Agent, Task
from upsonic.tools.mcp import MCPHandler

math_agent = MCPHandler(command="python math_agent_server.py")

orchestrator = Agent(
    model="anthropic/claude-sonnet-4-5",
    name="Orchestrator",
)

task = Task(
    description="Use the do tool to ask: What is 12 * 15?",
    tools=[math_agent],
)

result = orchestrator.do(task)
print(result)
The MCPHandler spawns the server as a subprocess, discovers the do tool, and makes it available to the orchestrator agent.

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Multiple Agent Servers as Tools

Use tool_name_prefix to avoid name collisions when combining multiple agent servers: 
from upsonic import Agent, Task
from upsonic.tools.mcp import MCPHandler

math = MCPHandler(command="python math_server.py", tool_name_prefix="math")
writer = MCPHandler(command="python writer_server.py", tool_name_prefix="writer")

orchestrator = Agent(
    model="anthropic/claude-sonnet-4-5",
    name="Orchestrator",
    tools=[math, writer],
)

result = orchestrator.do("Calculate 2^10 and write a sentence about the result.")

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Customizing the Server

as_mcp() returns a standard FastMCP server object. You can add extra tools before running: 
from upsonic import Agent

agent = Agent(name="Assistant", model="anthropic/claude-sonnet-4-5")
mcp = agent.as_mcp()

@mcp.tool
def get_current_date() -> str:
    """Get today's date."""
    from datetime import date
    return str(date.today())

mcp.run()

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How It Works

  1. as_mcp() creates a FastMCP server named after the agent.
  2. It registers a do tool whose description includes the agent’s role, goal, and instructions.
  3. When a client calls do(task="..."), the agent runs its full workflow internally (reasoning, tool use, memory) and returns the result as text.
  4. .run() starts the server and blocks, waiting for client connections.
For more information on MCP tools, see MCP Tools Overview.