list_tools() response at connect time, so the calling agent never sees stale tool docs.
mcp_server.py
"""
MCP Context Provider
====================
MCPContextProvider wraps a single MCP server as a context provider.
Instructions for the sub-agent are built dynamically from the
server's `list_tools()` response at connect time, so the calling
agent never sees stale tool docs.
Lifecycle — `asetup` / `aclose` are called explicitly in this
cookbook. In a real app they'd usually run from the framework's
lifespan hook (FastAPI startup/shutdown, etc.) so every registered
provider gets set up and torn down on the same task that owns the
session. That task-ownership matters: the `mcp` SDK uses anyio
cancel scopes internally, and they must exit on the task that
entered them.
This cookbook uses `mode=ContextMode.tools` so the MCP server's
tools land flat on the calling agent. Default mode (`mode=default`)
instead wraps them in a `query_mcp_<id>` sub-agent tool — use that
when composing multiple MCP servers on one caller to avoid tool-name
collisions.
Requires:
OPENAI_API_KEY
uvx (the MCP time server is invoked via `uvx mcp-server-time`;
any stdio MCP command works)
"""
from __future__ import annotations
import asyncio
from agno.agent import Agent
from agno.context import ContextMode
from agno.context.mcp import MCPContextProvider
from agno.models.openai import OpenAIResponses
async def main() -> None:
# ------------------------------------------------------------------
# Create the provider (unconnected)
# ------------------------------------------------------------------
provider = MCPContextProvider(
server_name="time",
transport="stdio",
command="uvx",
args=["mcp-server-time"],
mode=ContextMode.tools,
model=OpenAIResponses(id="gpt-5.4-mini"),
)
# ------------------------------------------------------------------
# Bracket with asetup / aclose so the MCP session lives on this
# task. Multiple calls to asetup() are safe.
# ------------------------------------------------------------------
await provider.asetup()
try:
print(f"astatus() = {await provider.astatus()}\n")
agent = Agent(
model=OpenAIResponses(id="gpt-5.4"),
tools=provider.get_tools(),
instructions=provider.instructions(),
markdown=True,
)
prompt = "What time is it in Tokyo right now?"
print(f"> {prompt}\n")
await agent.aprint_response(prompt)
finally:
await provider.aclose()
if __name__ == "__main__":
asyncio.run(main())
Run the Example
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv pip install -U agno openai
3
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
4
Run the example
Save the code above as
mcp_server.py, then run:python mcp_server.py