Skip to content

GitLab

"docs/vscode:/vscode.git/clone" did not exist on "084aa19f02b00198b36bd8d742d4169d6f5a32ce"
Unverified Commit a4ec0c55 authored by daniel-salib's avatar daniel-salib Committed by GitHub
Browse files

[Frontend] Add MCP tool streaming support to Responses API (#31761) 


Signed-off-by: default avatarDaniel Salib <danielsalib@meta.com>
parent 0fa8dd24
Expand all Hide whitespace changes
Inline Side-by-side
Showing with 1387 additions and 629 deletions
tests/entrypoints/openai/test_response_api_mcp_tools.py
View file @ a4ec0c55
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import pytest_asyncio
from openai import OpenAI
......@@ -13,199 +14,6 @@ from ...utils import RemoteOpenAIServer
MODEL_NAME = "openai/gpt-oss-20b"
@pytest.fixture(scope="module")
def monkeypatch_module():
from _pytest.monkeypatch import MonkeyPatch
mpatch = MonkeyPatch()
yield mpatch
mpatch.undo()
@pytest.fixture(scope="module")
def mcp_disabled_server(monkeypatch_module: pytest.MonkeyPatch):
args = ["--enforce-eager", "--tool-server", "demo"]
with monkeypatch_module.context() as m:
m.setenv("VLLM_ENABLE_RESPONSES_API_STORE", "1")
m.setenv("PYTHON_EXECUTION_BACKEND", "dangerously_use_uv")
# Helps the model follow instructions better
m.setenv("VLLM_GPT_OSS_HARMONY_SYSTEM_INSTRUCTIONS", "1")
with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
yield remote_server
@pytest.fixture(scope="function")
def mcp_enabled_server(monkeypatch_module: pytest.MonkeyPatch):
args = ["--enforce-eager", "--tool-server", "demo"]
with monkeypatch_module.context() as m:
m.setenv("VLLM_ENABLE_RESPONSES_API_STORE", "1")
m.setenv("PYTHON_EXECUTION_BACKEND", "dangerously_use_uv")
m.setenv("VLLM_GPT_OSS_SYSTEM_TOOL_MCP_LABELS", "code_interpreter,container")
# Helps the model follow instructions better
m.setenv("VLLM_GPT_OSS_HARMONY_SYSTEM_INSTRUCTIONS", "1")
with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
yield remote_server
@pytest_asyncio.fixture
async def mcp_disabled_client(mcp_disabled_server):
async with mcp_disabled_server.get_async_client() as async_client:
yield async_client
@pytest_asyncio.fixture
async def mcp_enabled_client(mcp_enabled_server):
async with mcp_enabled_server.get_async_client() as async_client:
yield async_client
@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
async def test_mcp_tool_env_flag_enabled(mcp_enabled_client: OpenAI, model_name: str):
response = await mcp_enabled_client.responses.create(
model=model_name,
input=(
"Execute the following code: "
"import random; print(random.randint(1, 1000000))"
),
instructions=(
"You must use the Python tool to execute code. Never simulate execution."
),
tools=[
{
"type": "mcp",
"server_label": "code_interpreter",
# URL unused for DemoToolServer
"server_url": "http://localhost:8888",
}
],
extra_body={"enable_response_messages": True},
)
assert response is not None
assert response.status == "completed"
# Verify output messages: Tool calls and responses on analysis channel
tool_call_found = False
tool_response_found = False
for message in response.output_messages:
recipient = message.get("recipient")
if recipient and recipient.startswith("python"):
tool_call_found = True
assert message.get("channel") == "analysis", (
"Tool call should be on analysis channel"
)
author = message.get("author", {})
if (
author.get("role") == "tool"
and author.get("name")
and author.get("name").startswith("python")
):
tool_response_found = True
assert message.get("channel") == "analysis", (
"Tool response should be on analysis channel"
)
assert tool_call_found, "Should have found at least one Python tool call"
assert tool_response_found, "Should have found at least one Python tool response"
for message in response.input_messages:
assert message.get("author").get("role") != "developer", (
"No developer messages should be present with valid mcp tool"
)
@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
async def test_mcp_tool_with_allowed_tools_star(
mcp_enabled_client: OpenAI, model_name: str
):
"""Test MCP tool with allowed_tools=['*'] to select all available tools.
This E2E test verifies that the "*" wildcard works end-to-end.
See test_serving_responses.py for detailed unit tests of "*" normalization.
"""
response = await mcp_enabled_client.responses.create(
model=model_name,
input=(
"Execute the following code: "
"import random; print(random.randint(1, 1000000))"
),
instructions=(
"You must use the Python tool to execute code. Never simulate execution."
),
tools=[
{
"type": "mcp",
"server_label": "code_interpreter",
"server_url": "http://localhost:8888",
# Using "*" to allow all tools from this MCP server
"allowed_tools": ["*"],
}
],
extra_body={"enable_response_messages": True},
)
assert response is not None
assert response.status == "completed"
# Verify tool calls work with allowed_tools=["*"]
tool_call_found = False
for message in response.output_messages:
recipient = message.get("recipient")
if recipient and recipient.startswith("python"):
tool_call_found = True
break
assert tool_call_found, "Should have found at least one Python tool call with '*'"
@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
async def test_mcp_tool_env_flag_disabled(mcp_disabled_client: OpenAI, model_name: str):
response = await mcp_disabled_client.responses.create(
model=model_name,
input=(
"Execute the following code if the tool is present: "
"import random; print(random.randint(1, 1000000))"
),
tools=[
{
"type": "mcp",
"server_label": "code_interpreter",
# URL unused for DemoToolServer
"server_url": "http://localhost:8888",
}
],
extra_body={"enable_response_messages": True},
)
assert response is not None
assert response.status == "completed"
# Verify output messages: No tool calls and responses
tool_call_found = False
tool_response_found = False
for message in response.output_messages:
recipient = message.get("recipient")
if recipient and recipient.startswith("python"):
tool_call_found = True
assert message.get("channel") == "analysis", (
"Tool call should be on analysis channel"
)
author = message.get("author", {})
if (
author.get("role") == "tool"
and author.get("name")
and author.get("name").startswith("python")
):
tool_response_found = True
assert message.get("channel") == "analysis", (
"Tool response should be on analysis channel"
)
assert not tool_call_found, "Should not have a python call"
assert not tool_response_found, "Should not have a tool response"
for message in response.input_messages:
assert message.get("author").get("role") != "developer", (
"No developer messages should be present without a valid tool"
)
def test_get_tool_description():
"""Test MCPToolServer.get_tool_description filtering logic.
......@@ -249,7 +57,10 @@ def test_get_tool_description():
assert result.tools[1].name == "tool3"
# Single tool
result = server.get_tool_description("test_server", allowed_tools=["tool2"])
result = server.get_tool_description(
"test_server",
allowed_tools=["tool2"],
)
assert len(result.tools) == 1
assert result.tools[0].name == "tool2"
......@@ -259,3 +70,283 @@ def test_get_tool_description():
# Empty list - returns None
assert server.get_tool_description("test_server", allowed_tools=[]) is None
class TestMCPEnabled:
"""Tests that require MCP tools to be enabled via environment variable."""
@pytest.fixture(scope="class")
def monkeypatch_class(self):
from _pytest.monkeypatch import MonkeyPatch
mpatch = MonkeyPatch()
yield mpatch
mpatch.undo()
@pytest.fixture(scope="class")
def mcp_enabled_server(self, monkeypatch_class: pytest.MonkeyPatch):
args = ["--enforce-eager", "--tool-server", "demo"]
with monkeypatch_class.context() as m:
m.setenv("VLLM_ENABLE_RESPONSES_API_STORE", "1")
m.setenv("PYTHON_EXECUTION_BACKEND", "dangerously_use_uv")
m.setenv(
"VLLM_GPT_OSS_SYSTEM_TOOL_MCP_LABELS", "code_interpreter,container"
)
# Helps the model follow instructions better
m.setenv("VLLM_GPT_OSS_HARMONY_SYSTEM_INSTRUCTIONS", "1")
with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
yield remote_server
@pytest_asyncio.fixture
async def mcp_enabled_client(self, mcp_enabled_server):
async with mcp_enabled_server.get_async_client() as async_client:
yield async_client
@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
async def test_mcp_tool_env_flag_enabled(
self, mcp_enabled_client: OpenAI, model_name: str
):
response = await mcp_enabled_client.responses.create(
model=model_name,
input=(
"Execute the following code: "
"import random; print(random.randint(1, 1000000))"
),
instructions=(
"You must use the Python tool to execute code. "
"Never simulate execution."
),
tools=[
{
"type": "mcp",
"server_label": "code_interpreter",
# URL unused for DemoToolServer
"server_url": "http://localhost:8888",
}
],
extra_body={"enable_response_messages": True},
)
assert response is not None
assert response.status == "completed"
# Verify output messages: Tool calls and responses on analysis channel
tool_call_found = False
tool_response_found = False
for message in response.output_messages:
recipient = message.get("recipient")
if recipient and recipient.startswith("python"):
tool_call_found = True
assert message.get("channel") == "analysis", (
"Tool call should be on analysis channel"
)
author = message.get("author", {})
if (
author.get("role") == "tool"
and author.get("name")
and author.get("name").startswith("python")
):
tool_response_found = True
assert message.get("channel") == "analysis", (
"Tool response should be on analysis channel"
)
assert tool_call_found, "Should have found at least one Python tool call"
assert tool_response_found, (
"Should have found at least one Python tool response"
)
for message in response.input_messages:
assert message.get("author").get("role") != "developer", (
"No developer messages should be present with valid mcp tool"
)
@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
async def test_mcp_tool_with_allowed_tools_star(
self, mcp_enabled_client: OpenAI, model_name: str
):
"""Test MCP tool with allowed_tools=['*'] to select all available
tools.
This E2E test verifies that the "*" wildcard works end-to-end.
See test_serving_responses.py for detailed unit tests of "*"
normalization.
"""
response = await mcp_enabled_client.responses.create(
model=model_name,
input=(
"Execute the following code: "
"import random; print(random.randint(1, 1000000))"
),
instructions=(
"You must use the Python tool to execute code. "
"Never simulate execution."
),
tools=[
{
"type": "mcp",
"server_label": "code_interpreter",
"server_url": "http://localhost:8888",
# Using "*" to allow all tools from this MCP server
"allowed_tools": ["*"],
}
],
extra_body={"enable_response_messages": True},
)
assert response is not None
assert response.status == "completed"
# Verify tool calls work with allowed_tools=["*"]
tool_call_found = False
for message in response.output_messages:
recipient = message.get("recipient")
if recipient and recipient.startswith("python"):
tool_call_found = True
break
assert tool_call_found, (
"Should have found at least one Python tool call with '*'"
)
@pytest.mark.flaky(reruns=3)
@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
async def test_mcp_tool_calling_streaming_types(
self, mcp_enabled_client: OpenAI, model_name: str
):
pairs_of_event_types = {
"response.completed": "response.created",
"response.output_item.done": "response.output_item.added",
"response.content_part.done": "response.content_part.added",
"response.output_text.done": "response.output_text.delta",
"response.reasoning_text.done": "response.reasoning_text.delta",
"response.reasoning_part.done": "response.reasoning_part.added",
"response.mcp_call_arguments.done": ("response.mcp_call_arguments.delta"),
"response.mcp_call.completed": "response.mcp_call.in_progress",
}
tools = [
{
"type": "mcp",
"server_label": "code_interpreter",
}
]
input_text = "What is 13 * 24? Use python to calculate the result."
stream_response = await mcp_enabled_client.responses.create(
model=model_name,
input=input_text,
tools=tools,
stream=True,
instructions=(
"You must use the Python tool to execute code. "
"Never simulate execution."
),
)
stack_of_event_types = []
saw_mcp_type = False
async for event in stream_response:
if event.type == "response.created":
stack_of_event_types.append(event.type)
elif event.type == "response.completed":
assert stack_of_event_types[-1] == pairs_of_event_types[event.type]
stack_of_event_types.pop()
elif (
event.type.endswith("added")
or event.type == "response.mcp_call.in_progress"
):
stack_of_event_types.append(event.type)
elif event.type.endswith("delta"):
if stack_of_event_types[-1] == event.type:
continue
stack_of_event_types.append(event.type)
elif (
event.type.endswith("done")
or event.type == "response.mcp_call.completed"
):
assert stack_of_event_types[-1] == pairs_of_event_types[event.type]
if "mcp_call" in event.type:
saw_mcp_type = True
stack_of_event_types.pop()
assert len(stack_of_event_types) == 0
assert saw_mcp_type, "Should have seen at least one mcp call"
class TestMCPDisabled:
"""Tests that verify behavior when MCP tools are disabled."""
@pytest.fixture(scope="class")
def monkeypatch_class(self):
from _pytest.monkeypatch import MonkeyPatch
mpatch = MonkeyPatch()
yield mpatch
mpatch.undo()
@pytest.fixture(scope="class")
def mcp_disabled_server(self, monkeypatch_class: pytest.MonkeyPatch):
args = ["--enforce-eager", "--tool-server", "demo"]
with monkeypatch_class.context() as m:
m.setenv("VLLM_ENABLE_RESPONSES_API_STORE", "1")
m.setenv("PYTHON_EXECUTION_BACKEND", "dangerously_use_uv")
# Helps the model follow instructions better
m.setenv("VLLM_GPT_OSS_HARMONY_SYSTEM_INSTRUCTIONS", "1")
with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
yield remote_server
@pytest_asyncio.fixture
async def mcp_disabled_client(self, mcp_disabled_server):
async with mcp_disabled_server.get_async_client() as async_client:
yield async_client
@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
async def test_mcp_tool_env_flag_disabled(
self, mcp_disabled_client: OpenAI, model_name: str
):
response = await mcp_disabled_client.responses.create(
model=model_name,
input=(
"Execute the following code if the tool is present: "
"import random; print(random.randint(1, 1000000))"
),
tools=[
{
"type": "mcp",
"server_label": "code_interpreter",
# URL unused for DemoToolServer
"server_url": "http://localhost:8888",
}
],
extra_body={"enable_response_messages": True},
)
assert response is not None
assert response.status == "completed"
# Verify output messages: No tool calls and responses
tool_call_found = False
tool_response_found = False
for message in response.output_messages:
recipient = message.get("recipient")
if recipient and recipient.startswith("python"):
tool_call_found = True
assert message.get("channel") == "analysis", (
"Tool call should be on analysis channel"
)
author = message.get("author", {})
if (
author.get("role") == "tool"
and author.get("name")
and author.get("name").startswith("python")
):
tool_response_found = True
assert message.get("channel") == "analysis", (
"Tool response should be on analysis channel"
)
assert not tool_call_found, "Should not have a python call"
assert not tool_response_found, "Should not have a tool response"
for message in response.input_messages:
assert message.get("author").get("role") != "developer", (
"No developer messages should be present without a valid tool"
)
tests/entrypoints/openai/test_response_api_with_harmony.py
View file @ a4ec0c55
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import importlib
import importlib.util
import json
import time
......@@ -44,6 +43,8 @@ def server():
env_dict = dict(
VLLM_ENABLE_RESPONSES_API_STORE="1",
PYTHON_EXECUTION_BACKEND="dangerously_use_uv",
VLLM_GPT_OSS_SYSTEM_TOOL_MCP_LABELS="code_interpreter,container,web_search_preview",
VLLM_GPT_OSS_HARMONY_SYSTEM_INSTRUCTIONS="1",
)
with RemoteOpenAIServer(MODEL_NAME, args, env_dict=env_dict) as remote_server:
......@@ -855,6 +856,237 @@ async def test_function_calling_with_stream(client: OpenAI, model_name: str):
assert event.response.output_text is not None
@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
async def test_function_calling_no_code_interpreter_events(
client: OpenAI, model_name: str
):
"""Verify that function calls don't trigger code_interpreter events.
This test ensures that function calls (functions.*) use their own
function_call event types and don't incorrectly emit code_interpreter
events during streaming.
"""
tools = [GET_WEATHER_SCHEMA]
input_list = [
{
"role": "user",
"content": "What's the weather like in Paris today?",
}
]
stream_response = await client.responses.create(
model=model_name,
input=input_list,
tools=tools,
stream=True,
)
# Track which event types we see
event_types_seen = set()
function_call_found = False
async for event in stream_response:
event_types_seen.add(event.type)
if (
event.type == "response.output_item.added"
and event.item.type == "function_call"
):
function_call_found = True
# Ensure NO code_interpreter events are emitted for function calls
assert "code_interpreter" not in event.type, (
"Found code_interpreter event "
f"'{event.type}' during function call. Function calls should only "
"emit function_call events, not code_interpreter events."
)
# Verify we actually saw a function call
assert function_call_found, "Expected to see a function_call in the stream"
# Verify we saw the correct function call event types
assert (
"response.function_call_arguments.delta" in event_types_seen
or "response.function_call_arguments.done" in event_types_seen
), "Expected to see function_call_arguments events"
@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
async def test_mcp_code_interpreter_streaming(client: OpenAI, model_name: str, server):
tools = [
{
"type": "mcp",
"server_label": "code_interpreter",
}
]
input_text = (
"Calculate 15 * 32 using python. "
"The python interpreter is not stateful and you must print to see the output."
)
stream_response = await client.responses.create(
model=model_name,
input=input_text,
tools=tools,
stream=True,
temperature=0.0,
instructions=(
"You must use the Python tool to execute code. Never simulate execution."
),
)
mcp_call_added = False
mcp_call_in_progress = False
mcp_arguments_delta_seen = False
mcp_arguments_done = False
mcp_call_completed = False
mcp_item_done = False
code_interpreter_events_seen = False
async for event in stream_response:
if "code_interpreter" in event.type:
code_interpreter_events_seen = True
if event.type == "response.output_item.added":
if hasattr(event.item, "type") and event.item.type == "mcp_call":
mcp_call_added = True
assert event.item.name == "python"
assert event.item.server_label == "code_interpreter"
elif event.type == "response.mcp_call.in_progress":
mcp_call_in_progress = True
elif event.type == "response.mcp_call_arguments.delta":
mcp_arguments_delta_seen = True
assert event.delta is not None
elif event.type == "response.mcp_call_arguments.done":
mcp_arguments_done = True
assert event.name == "python"
assert event.arguments is not None
elif event.type == "response.mcp_call.completed":
mcp_call_completed = True
elif (
event.type == "response.output_item.done"
and hasattr(event.item, "type")
and event.item.type == "mcp_call"
):
mcp_item_done = True
assert event.item.name == "python"
assert event.item.status == "completed"
assert mcp_call_added, "MCP call was not added"
assert mcp_call_in_progress, "MCP call in_progress event not seen"
assert mcp_arguments_delta_seen, "MCP arguments delta event not seen"
assert mcp_arguments_done, "MCP arguments done event not seen"
assert mcp_call_completed, "MCP call completed event not seen"
assert mcp_item_done, "MCP item done event not seen"
assert not code_interpreter_events_seen, (
"Should not see code_interpreter events when using MCP type"
)
@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
async def test_mcp_tool_multi_turn(client: OpenAI, model_name: str, server):
"""Test MCP tool calling across multiple turns.
This test verifies that MCP tools work correctly in multi-turn conversations,
maintaining state across turns via the previous_response_id mechanism.
"""
tools = [
{
"type": "mcp",
"server_label": "code_interpreter",
}
]
# First turn - make a calculation
response1 = await client.responses.create(
model=model_name,
input="Calculate 123 * 456 using python and print the result.",
tools=tools,
temperature=0.0,
instructions=(
"You must use the Python tool to execute code. Never simulate execution."
),
extra_body={"enable_response_messages": True},
)
assert response1 is not None
assert response1.status == "completed"
# Verify MCP call in first response by checking output_messages
tool_call_found = False
tool_response_found = False
for message in response1.output_messages:
recipient = message.get("recipient")
if recipient and recipient.startswith("python"):
tool_call_found = True
author = message.get("author", {})
if (
author.get("role") == "tool"
and author.get("name")
and author.get("name").startswith("python")
):
tool_response_found = True
# Verify MCP tools were actually used
assert tool_call_found, "MCP tool call not found in output_messages"
assert tool_response_found, "MCP tool response not found in output_messages"
# Verify input messages: Should have system message with tool, NO developer message
developer_messages = [
msg for msg in response1.input_messages if msg["author"]["role"] == "developer"
]
assert len(developer_messages) == 0, (
"No developer message expected for elevated tools"
)
# Second turn - reference previous calculation
response2 = await client.responses.create(
model=model_name,
input="Now divide that result by 2.",
tools=tools,
temperature=0.0,
instructions=(
"You must use the Python tool to execute code. Never simulate execution."
),
previous_response_id=response1.id,
extra_body={"enable_response_messages": True},
)
assert response2 is not None
assert response2.status == "completed"
# Verify input messages are correct: should have two messages -
# one to the python recipient on analysis channel and one from tool role
mcp_recipient_messages = []
tool_role_messages = []
for msg in response2.input_messages:
if msg["author"]["role"] == "assistant":
# Check if this is a message to MCP recipient on analysis channel
if msg.get("channel") == "analysis" and msg.get("recipient"):
recipient = msg.get("recipient")
if recipient.startswith("code_interpreter") or recipient == "python":
mcp_recipient_messages.append(msg)
elif msg["author"]["role"] == "tool":
tool_role_messages.append(msg)
assert len(mcp_recipient_messages) > 0, (
"Expected message(s) to MCP recipient on analysis channel"
)
assert len(tool_role_messages) > 0, (
"Expected message(s) from tool role after MCP call"
)
@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
async def test_output_messages_enabled(client: OpenAI, model_name: str, server):
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment