[Misc] Add 20 regression tests for 11 tool parser bug fixes (#38172)

Signed-off-by: Ben Browning <bbrownin@redhat.com>
Co-authored-by: Chauncey <chaunceyjiang@gmail.com>

tests/tool_parsers/test_deepseekv32_tool_parser.py

@@ -11,6 +11,7 @@ from unittest.mock import MagicMock
 
 import pytest
 
+from tests.tool_parsers.utils import run_tool_extraction_streaming
 from vllm.entrypoints.openai.chat_completion.protocol import (
     ChatCompletionToolsParam,
     FunctionDefinition,
@@ -26,6 +27,7 @@ from vllm.tool_parsers.deepseekv32_tool_parser import DeepSeekV32ToolParser
 # tokenizer object to be truthy (the parser checks `if not self.model_tokenizer`).
 MOCK_TOKENIZER = MagicMock()
 MOCK_TOKENIZER.get_vocab.return_value = {}
+MOCK_TOKENIZER.tokenize.return_value = []
 
 
 def make_parser(tools=None) -> DeepSeekV32ToolParser:
@@ -483,6 +485,88 @@ class TestExtractToolCallsStreaming:
         assert all(not d.tool_calls for d in deltas)
 
 
+class TestDelimiterPreservation:
+    """Regression: fast detokenization skipping DSML delimiters (PR #33964)."""
+
+    @pytest.fixture
+    def parser(self):
+        return make_parser()
+
+    def test_delimiter_preserved_fast_detokenization(self, parser):
+        """DSML delimiters as literal text must still be detected."""
+        # Delimiters appear as regular text (fast detokenization scenario).
+        model_output = (
+            f"{FC_START}\n"
+            f'{INV_START}get_weather">\n'
+            f'{PARAM_START}location" string="true">Tokyo{PARAM_END}\n'
+            f"{INV_END}\n"
+            f"{FC_END}"
+        )
+
+        # Non-streaming: parser must detect the tool call
+        result = parser.extract_tool_calls(model_output, None)
+        assert result.tools_called
+        assert len(result.tool_calls) == 1
+        assert result.tool_calls[0].function.name == "get_weather"
+        assert json.loads(result.tool_calls[0].function.arguments) == {
+            "location": "Tokyo"
+        }
+
+        assert result.content is None
+
+        # With content prefix
+        prefixed_output = "Here is the weather: " + model_output
+        result2 = parser.extract_tool_calls(prefixed_output, None)
+        assert result2.tools_called
+        assert result2.content == "Here is the weather: "
+
+    def test_tool_detection_skip_special_tokens_false(self, parser):
+        """Regression: skip_special_tokens must be False when tools are enabled."""
+        # adjust_request must set skip_special_tokens=False
+        tool = make_tool_param(
+            "search",
+            {
+                "type": "object",
+                "properties": {
+                    "query": {"type": "string"},
+                },
+            },
+        )
+        request = make_request(tools=[tool])
+        request.tool_choice = "auto"
+        adjusted = parser.adjust_request(request)
+        assert adjusted.skip_special_tokens is False
+
+        full_text = build_tool_call("search", {"query": "vllm documentation"})
+
+        # Non-streaming extraction
+        non_stream_result = parser.extract_tool_calls(full_text, request)
+        assert non_stream_result.tools_called
+        assert len(non_stream_result.tool_calls) == 1
+        assert non_stream_result.tool_calls[0].function.name == "search"
+        ns_args = json.loads(non_stream_result.tool_calls[0].function.arguments)
+        assert ns_args == {"query": "vllm documentation"}
+
+        # Streaming extraction: drive the parser line-by-line
+        chunks: list[str] = []
+        remaining = full_text
+        while remaining:
+            nl = remaining.find("\n")
+            if nl == -1:
+                chunks.append(remaining)
+                break
+            chunks.append(remaining[: nl + 1])
+            remaining = remaining[nl + 1 :]
+
+        reconstructor = run_tool_extraction_streaming(
+            parser, chunks, request, assert_one_tool_per_delta=False
+        )
+        assert len(reconstructor.tool_calls) == 1
+        assert reconstructor.tool_calls[0].function.name == "search"
+        streamed_args = json.loads(reconstructor.tool_calls[0].function.arguments)
+        assert streamed_args == ns_args
+
+
 @pytest.fixture(scope="module")
 def deepseekv32_tokenizer():
     return get_tokenizer(tokenizer_name="deepseek-ai/DeepSeek-V3.2")

tests/tool_parsers/test_glm4_moe_tool_parser.py

@@ -822,3 +822,108 @@ def test_extract_tool_calls_numeric_deserialization(glm4_moe_tool_parser, mock_r
     # Boolean should be deserialized as bool
     assert args["enabled"] is True
     assert isinstance(args["enabled"], bool)
+
+
+def test_zero_argument_tool_call(glm4_moe_tool_parser, mock_request):
+    """Regression: zero-argument tool call crash (PR #32321)."""
+    model_output = """<tool_call>get_time
+</tool_call>"""
+
+    extracted = glm4_moe_tool_parser.extract_tool_calls(
+        model_output, request=mock_request
+    )  # type: ignore[arg-type]
+
+    assert extracted.tools_called
+    assert len(extracted.tool_calls) == 1
+    assert extracted.tool_calls[0].function.name == "get_time"
+    args = json.loads(extracted.tool_calls[0].function.arguments)
+    assert args == {}
+
+
+def test_malformed_tool_call_no_regex_match(glm4_moe_tool_parser, mock_request):
+    """Regression: malformed tool_call with no regex match (PR #32321)."""
+    model_output = "<tool_call>   </tool_call>"
+
+    extracted = glm4_moe_tool_parser.extract_tool_calls(
+        model_output, request=mock_request
+    )  # type: ignore[arg-type]
+
+    assert extracted.tools_called is False
+    assert extracted.tool_calls == []
+
+
+def test_delimiter_preserved_transformers_5x(glm4_moe_tool_parser):
+    """Regression: adjust_request sets skip_special_tokens=False (PR #31622)."""
+    # Tools enabled
+    request_with_tools = ChatCompletionRequest(
+        model=MODEL,
+        messages=[],
+        tools=[
+            {
+                "type": "function",
+                "function": {
+                    "name": "get_weather",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {"city": {"type": "string"}},
+                    },
+                },
+            }
+        ],
+    )  # type: ignore
+    adjusted = glm4_moe_tool_parser.adjust_request(request_with_tools)
+    assert adjusted.skip_special_tokens is False
+
+    # tool_choice="none"
+    request_no_choice = ChatCompletionRequest(
+        model=MODEL,
+        messages=[],
+        tools=[
+            {
+                "type": "function",
+                "function": {
+                    "name": "get_weather",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {"city": {"type": "string"}},
+                    },
+                },
+            }
+        ],
+        tool_choice="none",
+    )  # type: ignore
+    adjusted_none = glm4_moe_tool_parser.adjust_request(request_no_choice)
+    assert adjusted_none.skip_special_tokens is True
+
+    # No tools at all
+    request_no_tools = ChatCompletionRequest(
+        model=MODEL,
+        messages=[],
+    )  # type: ignore
+    adjusted_empty = glm4_moe_tool_parser.adjust_request(request_no_tools)
+    assert adjusted_empty.skip_special_tokens is True
+
+
+def test_unicode_characters_preserved(glm4_moe_tool_parser, mock_request):
+    """Regression: Unicode chars must not be escaped to \\uXXXX (PR #30920)."""
+    model_output = """<tool_call>send_message
+<arg_key>greeting</arg_key>
+<arg_value>你好世界</arg_value>
+<arg_key>emoji</arg_key>
+<arg_value>🎉</arg_value>
+</tool_call>"""
+
+    extracted = glm4_moe_tool_parser.extract_tool_calls(
+        model_output, request=mock_request
+    )  # type: ignore[arg-type]
+
+    assert extracted.tools_called
+    assert len(extracted.tool_calls) == 1
+
+    raw_args = extracted.tool_calls[0].function.arguments
+    assert "你好世界" in raw_args
+    assert "🎉" in raw_args
+    assert "\\u4f60" not in raw_args
+    parsed_args = json.loads(raw_args)
+    assert parsed_args["greeting"] == "你好世界"
+    assert parsed_args["emoji"] == "🎉"

tests/tool_parsers/test_kimi_k2_tool_parser.py

@@ -872,6 +872,59 @@ def test_streaming_tool_call_markers_not_leaked(kimi_k2_tool_parser):
     assert "I'll check the weather." in full_content or len(all_content) > 0
 
 
+def test_native_id_extracted_and_placed_on_tool_call(kimi_k2_tool_parser):
+    """Regression: parser extracts native ID onto ToolCall (PR #32768)."""
+    model_output = (
+        "Checking weather. "
+        "<|tool_calls_section_begin|>"
+        "<|tool_call_begin|>functions.get_weather:0"
+        '<|tool_call_argument_begin|>{"city": "Tokyo"}'
+        "<|tool_call_end|>"
+        "<|tool_calls_section_end|>"
+    )
+
+    result = kimi_k2_tool_parser.extract_tool_calls(model_output, request=None)
+    assert result.tools_called
+    assert len(result.tool_calls) == 1
+
+    tc = result.tool_calls[0]
+    # Native ID from model output must be used as the tool call ID
+    assert tc.id == "functions.get_weather:0"
+    assert tc.function.name == "get_weather"
+    assert json.loads(tc.function.arguments) == {"city": "Tokyo"}
+
+
+def test_multi_turn_native_id_continuity(kimi_k2_tool_parser, kimi_k2_tokenizer):
+    """Regression: native IDs from turn 1 preserved across turns (PR #32768)."""
+    turn1_output = (
+        "Let me check. "
+        "<|tool_calls_section_begin|>"
+        "<|tool_call_begin|>functions.get_weather:0"
+        '<|tool_call_argument_begin|>{"city": "Beijing"}'
+        "<|tool_call_end|>"
+        "<|tool_calls_section_end|>"
+    )
+
+    turn1_result = kimi_k2_tool_parser.extract_tool_calls(turn1_output, request=None)
+    assert turn1_result.tools_called
+    assert turn1_result.tool_calls[0].id == "functions.get_weather:0"
+
+    # Fresh parser for turn 2
+    turn2_parser = KimiK2ToolParser(kimi_k2_tokenizer)
+    turn2_output = (
+        "Now let me get news. "
+        "<|tool_calls_section_begin|>"
+        "<|tool_call_begin|>functions.get_news:0"
+        '<|tool_call_argument_begin|>{"topic": "weather in Beijing"}'
+        "<|tool_call_end|>"
+        "<|tool_calls_section_end|>"
+    )
+
+    turn2_result = turn2_parser.extract_tool_calls(turn2_output, request=None)
+    assert turn2_result.tools_called
+    assert turn2_result.tool_calls[0].id == "functions.get_news:0"
+
+
 def test_streaming_multiple_tool_calls_not_leaked(kimi_k2_tool_parser):
     """
     Test that MULTIPLE tool calls in streaming mode do not leak into content.

tests/tool_parsers/test_minimax_m2_tool_parser.py

@@ -5,6 +5,10 @@ import json
 
 import pytest
 
+from vllm.entrypoints.openai.chat_completion.protocol import (
+    ChatCompletionToolsParam,
+    FunctionDefinition,
+)
 from vllm.tool_parsers.minimax_m2_tool_parser import (
     MinimaxM2ToolParser,
 )
@@ -442,3 +446,105 @@ class TestLargeChunks:
             "city": "Seattle",
             "days": "5",
         }
+
+
+class TestAnyOfNullableParam:
+    """Regression: anyOf nullable parameter parsing (PR #32342)."""
+
+    def test_anyof_nullable_param_non_null_value(self):
+        """A valid non-null string should be preserved, not collapsed to None."""
+        tools = [
+            ChatCompletionToolsParam(
+                function=FunctionDefinition(
+                    name="update_profile",
+                    parameters={
+                        "type": "object",
+                        "properties": {
+                            "nickname": {
+                                "anyOf": [{"type": "string"}, {"type": "null"}],
+                            },
+                        },
+                    },
+                ),
+            )
+        ]
+        parser = MinimaxM2ToolParser(FakeTokenizer(), tools=tools)
+
+        results = _feed(
+            parser,
+            [
+                '<minimax:tool_call><invoke name="update_profile">'
+                '<parameter name="nickname">Alice</parameter>'
+                "</invoke></minimax:tool_call>",
+            ],
+        )
+        tc = _collect_tool_calls(results)
+        assert len(tc) == 1
+        parsed = json.loads(tc[0]["arguments"])
+        assert parsed["nickname"] == "Alice"
+
+    def test_anyof_nullable_param_null_value(self):
+        """An actual null-like value should be returned as None/null."""
+        tools = [
+            ChatCompletionToolsParam(
+                function=FunctionDefinition(
+                    name="update_profile",
+                    parameters={
+                        "type": "object",
+                        "properties": {
+                            "nickname": {
+                                "anyOf": [{"type": "string"}, {"type": "null"}],
+                            },
+                        },
+                    },
+                ),
+            )
+        ]
+        parser = MinimaxM2ToolParser(FakeTokenizer(), tools=tools)
+
+        results = _feed(
+            parser,
+            [
+                '<minimax:tool_call><invoke name="update_profile">'
+                '<parameter name="nickname">null</parameter>'
+                "</invoke></minimax:tool_call>",
+            ],
+        )
+        tc = _collect_tool_calls(results)
+        assert len(tc) == 1
+        parsed = json.loads(tc[0]["arguments"])
+        assert parsed["nickname"] is None
+
+    def test_anyof_nullable_param_object_value(self):
+        """A valid object value in anyOf with null should parse as dict."""
+        tools = [
+            ChatCompletionToolsParam(
+                function=FunctionDefinition(
+                    name="update_settings",
+                    parameters={
+                        "type": "object",
+                        "properties": {
+                            "config": {
+                                "anyOf": [{"type": "object"}, {"type": "null"}],
+                            },
+                        },
+                    },
+                ),
+            )
+        ]
+        parser = MinimaxM2ToolParser(FakeTokenizer(), tools=tools)
+
+        results = _feed(
+            parser,
+            [
+                '<minimax:tool_call><invoke name="update_settings">'
+                '<parameter name="config">{"theme": "dark", "fontSize": 14}'
+                "</parameter>"
+                "</invoke></minimax:tool_call>",
+            ],
+        )
+        tc = _collect_tool_calls(results)
+        assert len(tc) == 1
+        parsed = json.loads(tc[0]["arguments"])
+        assert parsed["config"] == {"theme": "dark", "fontSize": 14}
+        assert isinstance(parsed["config"], dict)

tests/tool_parsers/test_mistral_tool_parser.py

@@ -890,3 +890,64 @@ def test_extract_tool_calls_streaming_pre_v11_tokenizer_one_chunk(
         assert expected_content == ""
     else:
         assert delta_message.content == expected_content
+
+
+def test_fast_detokenization_text_detection(mistral_tool_parser):
+    """Regression: bot_token in text but not token_ids (PR #37209)."""
+    model_output = '[TOOL_CALLS]add{"a": 1, "b": 2}'
+    # Token IDs that do NOT contain bot_token_id.
+    fake_token_ids = list(range(99, 99 + 20))
+
+    # First delta: pure content, no bot token yet
+    delta_message_before = mistral_tool_parser.extract_tool_calls_streaming(
+        previous_text="",
+        current_text="Hello",
+        delta_text="Hello",
+        previous_token_ids=[],
+        current_token_ids=[99],
+        delta_token_ids=[99],
+        request=None,
+    )
+    assert delta_message_before is not None
+    assert delta_message_before.content == "Hello"
+    assert not delta_message_before.tool_calls
+
+    # Second delta: bot token in text but NOT in token_ids
+    delta_message = mistral_tool_parser.extract_tool_calls_streaming(
+        previous_text="Hello",
+        current_text="Hello" + model_output,
+        delta_text=model_output,
+        previous_token_ids=[99],
+        current_token_ids=fake_token_ids,
+        delta_token_ids=fake_token_ids[1:],
+        request=None,
+    )
+    assert delta_message is not None
+    assert delta_message.tool_calls is not None
+    assert len(delta_message.tool_calls) > 0
+    assert delta_message.tool_calls[0].function is not None
+    assert delta_message.tool_calls[0].function.name == "add"
+
+
+def test_fast_detokenization_text_detection_pre_v11(
+    mistral_pre_v11_tool_parser,
+):
+    """Regression: bot_token text detection for pre-v11 tokenizer (PR #37209)."""
+    model_output = '[TOOL_CALLS] [{"name": "add", "arguments":{"a": 1, "b": 2}}]'
+
+    fake_token_ids = list(range(99, 99 + 30))
+
+    delta_message = mistral_pre_v11_tool_parser.extract_tool_calls_streaming(
+        previous_text="",
+        current_text=model_output,
+        delta_text=model_output,
+        previous_token_ids=[],
+        current_token_ids=fake_token_ids,
+        delta_token_ids=fake_token_ids,
+        request=None,
+    )
+    assert delta_message is not None
+    assert delta_message.tool_calls is not None
+    assert len(delta_message.tool_calls) > 0
+    assert delta_message.tool_calls[0].function is not None
+    assert delta_message.tool_calls[0].function.name == "add"

tests/tool_parsers/test_qwen3coder_tool_parser.py

@@ -974,3 +974,157 @@ fahrenheit
     assert args["city"] == "Dallas"
     assert args["state"] == "TX"
     assert args["unit"] == "fahrenheit"
+
+
+def test_malformed_xml_no_gt_delimiter(qwen3_tool_parser, sample_tools):
+    """Regression: malformed XML without '>' must not crash (PR #36774)."""
+    model_output = (
+        "<tool_call>\n"
+        "<function=get_current_weather\n"
+        "<parameter=city>Dallas</parameter>\n"
+        "</function>\n"
+        "</tool_call>"
+    )
+
+    request = ChatCompletionRequest(model=MODEL, messages=[], tools=sample_tools)
+    result = qwen3_tool_parser.extract_tool_calls(model_output, request=request)
+    assert result is not None
+    assert isinstance(result.tool_calls, list)
+    assert all(tc is not None for tc in result.tool_calls)
+
+
+def test_none_tool_calls_filtered(qwen3_tool_parser, sample_tools):
+    """Regression: None tool calls filtered from output (PR #36774)."""
+    model_output = (
+        "<tool_call>\n"
+        "<function=bad_func_no_gt\n"
+        "</function>\n"
+        "</tool_call>\n"
+        "<tool_call>\n"
+        "<function=get_current_weather>\n"
+        "<parameter=city>Dallas</parameter>\n"
+        "<parameter=state>TX</parameter>\n"
+        "</function>\n"
+        "</tool_call>"
+    )
+
+    request = ChatCompletionRequest(model=MODEL, messages=[], tools=sample_tools)
+    result = qwen3_tool_parser.extract_tool_calls(model_output, request=request)
+    assert all(tc is not None for tc in result.tool_calls)
+    assert result.tools_called
+    assert len(result.tool_calls) == 1
+    assert result.tool_calls[0].function.name == "get_current_weather"
+    args = json.loads(result.tool_calls[0].function.arguments)
+    assert args["city"] == "Dallas"
+    assert args["state"] == "TX"
+
+
+def test_anyof_parameter_not_double_encoded(qwen3_tokenizer):
+    """Regression: anyOf parameters must not be double-encoded (PR #36032)."""
+    tools = [
+        ChatCompletionToolsParam(
+            type="function",
+            function={
+                "name": "update_record",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "data": {
+                            "anyOf": [{"type": "object"}, {"type": "null"}],
+                        },
+                    },
+                },
+            },
+        )
+    ]
+
+    parser = Qwen3CoderToolParser(qwen3_tokenizer, tools=tools)
+
+    model_output = (
+        "<tool_call>\n"
+        "<function=update_record>\n"
+        '<parameter=data>{"key": "value", "count": 42}</parameter>\n'
+        "</function>\n"
+        "</tool_call>"
+    )
+
+    request = ChatCompletionRequest(model=MODEL, messages=[], tools=tools)
+    result = parser.extract_tool_calls(model_output, request=request)
+
+    assert result.tools_called
+    assert len(result.tool_calls) == 1
+    args = json.loads(result.tool_calls[0].function.arguments)
+    assert isinstance(args["data"], dict)
+    assert args["data"] == {"key": "value", "count": 42}
+
+
+def test_streaming_multi_param_single_chunk(
+    qwen3_tool_parser, qwen3_tokenizer, sample_tools
+):
+    """Regression: speculative decode delivering multiple params at once (PR #35615)."""
+    request = ChatCompletionRequest(model=MODEL, messages=[], tools=sample_tools)
+
+    deltas = [
+        "<tool_call>",
+        "\n<function=get_current_weather>",
+        "\n",  # triggers json_started -> sends "{"
+        # This single delta delivers all three parameters at once
+        "<parameter=city>\nDallas\n</parameter>"
+        "\n<parameter=state>\nTX\n</parameter>"
+        "\n<parameter=unit>\nfahrenheit\n</parameter>",
+        "\n</function>",
+        "\n</tool_call>",
+    ]
+
+    from tests.tool_parsers.utils import (
+        run_tool_extraction_streaming,
+    )
+
+    reconstructor = run_tool_extraction_streaming(
+        qwen3_tool_parser,
+        deltas,
+        request,
+        assert_one_tool_per_delta=False,
+    )
+
+    assert len(reconstructor.tool_calls) == 1
+    args = json.loads(reconstructor.tool_calls[0].function.arguments)
+    assert args["city"] == "Dallas"
+    assert args["state"] == "TX"
+    assert args["unit"] == "fahrenheit"
+
+
+def test_no_double_serialization_string_args(qwen3_tool_parser):
+    """Regression: string arguments must not be double-serialized (PR #35615)."""
+    tools = [
+        ChatCompletionToolsParam(
+            type="function",
+            function={
+                "name": "greet",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "message": {"type": "string"},
+                    },
+                },
+            },
+        )
+    ]
+
+    model_output = (
+        "<tool_call>\n"
+        "<function=greet>\n"
+        "<parameter=message>hello world</parameter>\n"
+        "</function>\n"
+        "</tool_call>"
+    )
+
+    request = ChatCompletionRequest(model=MODEL, messages=[], tools=tools)
+    result = qwen3_tool_parser.extract_tool_calls(model_output, request=request)
+
+    assert result.tools_called
+    assert len(result.tool_calls) == 1
+    raw_arguments = result.tool_calls[0].function.arguments
+    args = json.loads(raw_arguments)
+    assert args["message"] == "hello world"
+    assert '\\"hello world\\"' not in raw_arguments

tests/tool_parsers/test_step3p5_tool_parser.py

@@ -1431,3 +1431,140 @@ rectangle
     assert "<function=calculate_area>" not in extracted_tool_calls.content, (
         "Second tool call should not be in content"
     )
+
+
+def _accumulate_tool_states(delta_messages):
+    """Accumulate tool call state from a stream of DeltaMessage objects."""
+    content = ""
+    tool_states = {}
+    for delta_message in delta_messages:
+        if delta_message.content:
+            content += delta_message.content
+        if delta_message.tool_calls:
+            for tool_call in delta_message.tool_calls:
+                idx = tool_call.index
+                if idx not in tool_states:
+                    tool_states[idx] = {
+                        "id": None,
+                        "name": None,
+                        "arguments": "",
+                        "type": None,
+                    }
+                if tool_call.id:
+                    tool_states[idx]["id"] = tool_call.id
+                if tool_call.type:
+                    tool_states[idx]["type"] = tool_call.type
+                if tool_call.function:
+                    if tool_call.function.name:
+                        tool_states[idx]["name"] = tool_call.function.name
+                    if tool_call.function.arguments is not None:
+                        tool_states[idx]["arguments"] += tool_call.function.arguments
+    return content, tool_states
+
+
+def test_streaming_mtp_variable_chunks(
+    step3p5_tool_parser, step3p5_tokenizer, sample_tools
+):
+    """Regression: MTP variable-size chunks spanning param boundaries (PR #33690)."""
+    request = ChatCompletionRequest(model=MODEL, messages=[], tools=sample_tools)
+
+    delta_text_chunks = [
+        "<tool_call>\n<function=get_current_weather>\n<parameter=city>\n",
+        "Dallas\n</parameter>\n<parameter=state>\nTX",
+        "\n</parameter>\n<parameter=unit>\nfahrenheit\n</parameter>",
+        "\n</function>\n</tool_call>",
+    ]
+
+    _, tool_states = _accumulate_tool_states(
+        stream_delta_message_generator_from_chunks(
+            step3p5_tool_parser, step3p5_tokenizer, delta_text_chunks, request
+        )
+    )
+
+    assert len(tool_states) == 1
+
+    state = tool_states[0]
+    assert state["id"] is not None
+    assert state["type"] == "function"
+    assert state["name"] == "get_current_weather"
+
+    args = json.loads(state["arguments"])
+    assert args["city"] == "Dallas"
+    assert args["state"] == "TX"
+    assert args["unit"] == "fahrenheit"
+
+
+def test_streaming_multi_token_per_step(
+    step3p5_tool_parser, step3p5_tokenizer, sample_tools
+):
+    """Regression: MTP large chunks spanning multiple tool calls (PR #33690)."""
+    model_output = """<tool_call>
+<function=get_current_weather>
+<parameter=city>
+Dallas
+</parameter>
+<parameter=state>
+TX
+</parameter>
+<parameter=unit>
+fahrenheit
+</parameter>
+</function>
+</tool_call>
+<tool_call>
+<function=get_current_weather>
+<parameter=city>
+Orlando
+</parameter>
+<parameter=state>
+FL
+</parameter>
+<parameter=unit>
+celsius
+</parameter>
+</function>
+</tool_call>"""
+
+    request = ChatCompletionRequest(model=MODEL, messages=[], tools=sample_tools)
+
+    # MTP-style large chunks
+    mtp_chunks = [
+        (
+            "<tool_call>\n<function=get_current_weather>\n"
+            "<parameter=city>\nDallas\n</parameter>\n"
+            "<parameter=state>\nTX"
+        ),
+        (
+            "\n</parameter>\n<parameter=unit>\nfahrenheit\n</parameter>\n"
+            "</function>\n</tool_call>\n"
+            "<tool_call>\n<function=get_current_weather>\n"
+            "<parameter=city>\nOrlando\n</parameter>\n"
+            "<parameter=state>\nFL\n</parameter>\n"
+            "<parameter=unit>\ncelsius\n</parameter>\n"
+            "</function>\n</tool_call>"
+        ),
+    ]
+
+    _, mtp_tool_states = _accumulate_tool_states(
+        stream_delta_message_generator_from_chunks(
+            step3p5_tool_parser, step3p5_tokenizer, mtp_chunks, request
+        )
+    )
+
+    # Token-by-token streaming (reference)
+    step3p5_tool_parser_ref = Step3p5ToolParser(step3p5_tokenizer)
+    _, ref_tool_states = _accumulate_tool_states(
+        stream_delta_message_generator(
+            step3p5_tool_parser_ref, step3p5_tokenizer, model_output, request
+        )
+    )
+
+    assert len(mtp_tool_states) == 2
+    assert len(ref_tool_states) == 2
+
+    # MTP results must match reference
+    for idx in range(2):
+        assert mtp_tool_states[idx]["name"] == ref_tool_states[idx]["name"]
+        mtp_args = json.loads(mtp_tool_states[idx]["arguments"])
+        ref_args = json.loads(ref_tool_states[idx]["arguments"])
+        assert mtp_args == ref_args