> ## 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 Agent as MCP Server

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

## 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.

## Creating an MCP Server from an Agent

```python theme={null}
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.

## Using an Agent MCP Server from Another Agent

```python theme={null}
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.

## Multiple Agent Servers as Tools

Use `tool_name_prefix` to avoid name collisions when combining multiple agent servers:

```python theme={null}
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.")
```

## Customizing the Server

`as_mcp()` returns a standard `FastMCP` server object. You can add extra tools before running:

```python theme={null}
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()
```

## 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 [MCPHandler](/concepts/tools/mcp-tools/mcp-handler).