"""
Serve agents as MCP tools
=========================
Turn the default MCP surface off and serve agents directly as tools. A bare
agent in MCPConfig(tools=[...]) becomes a tool named after its id with the
agent's own description; agent.as_tool(name=..., description=...) publishes
it under a model-facing name and pitch of your choosing instead. An MCP
client sees chief and deep_research -- not run_agent(agent_id=...) -- and
each call runs through the same machinery as the default run tools (fresh
session minting, scope checks, progress). continue_run and cancel_run ride
along automatically so paused (human-in-the-loop) runs stay resumable; set
lifecycle_tools=False to serve exactly the configured tools.
as_tool also carries the presentation a client and a marketplace reviewer
read: title= is the display name, and annotations= are the behaviour hints
(readOnlyHint, destructiveHint, idempotentHint, openWorldHint) merged over
the defaults a published component asserts about itself.
Prerequisites: OPENAI_API_KEY
Run: .venvs/demo/bin/python cookbook/05_agent_os/14_mcp/agents_as_tools.py
Try: connect an MCP client to http://localhost:7777/mcp and call chief or deep_research
"""
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIResponses
from agno.os import AgentOS, MCPConfig
# ---------------------------------------------------------------------------
# Create the agents to expose
# ---------------------------------------------------------------------------
db = SqliteDb(
id="mcp-agents-as-tools-db",
db_file="tmp/mcp_agents_as_tools.db",
)
chief = Agent(
id="chief",
name="Chief",
model=OpenAIResponses(id="gpt-5.6-luna"),
db=db,
description="Answers executive questions and delegates follow-ups.",
instructions="Answer briefly and decisively.",
# Every exposed tool tells the client "pass session_id back to continue the
# conversation" -- history in context is what makes that promise real.
add_history_to_context=True,
)
researcher = Agent(
id="researcher",
name="Researcher",
model=OpenAIResponses(id="gpt-5.6-luna"),
db=db,
description="Digs into a topic and returns sourced findings.",
instructions="Be thorough and cite what you rely on.",
add_history_to_context=True,
)
# ---------------------------------------------------------------------------
# Serve the agents as the only MCP tools
# ---------------------------------------------------------------------------
agent_os = AgentOS(
id="mcp-agents-as-tools-os",
description="AgentOS serving its agents directly as MCP tools.",
db=db,
agents=[chief, researcher],
mcp=MCPConfig(
default_tools=False,
tools=[
chief,
researcher.as_tool(
name="deep_research",
title="Deep Research",
description="Thorough, sourced research. Send one clear question.",
# Hints are published to the client and read by assistant marketplaces
# during a listing review, which test them against what the tool really
# does -- so a wrong hint is worse than a missing one. The researcher
# holds no tools and only appends to its own session, so it refines the
# default rather than contradicting it: still not read-only (every run
# writes a session and a run row), but nothing it writes destroys
# anything, and running it twice is not the same as running it once.
annotations={"destructiveHint": False, "idempotentHint": False},
),
],
),
)
app = agent_os.get_app()
# ---------------------------------------------------------------------------
# Run Agents-as-Tools AgentOS
# ---------------------------------------------------------------------------
if __name__ == "__main__":
agent_os.serve(app=app)
