diff --git a/litellm/llms/ollama/completion/transformation.py b/litellm/llms/ollama/completion/transformation.py
index 204b0d15c03..72089574b3b 100644
--- a/litellm/llms/ollama/completion/transformation.py
+++ b/litellm/llms/ollama/completion/transformation.py
@@ -4,6 +4,7 @@
 from typing import TYPE_CHECKING, Any, AsyncIterator, Iterator, List, Optional, Union
 
 from httpx._models import Headers, Response
+from pydantic import BaseModel, ConfigDict, ValidationError
 
 import litellm
 from litellm._logging import verbose_proxy_logger
@@ -17,7 +18,12 @@
 )
 from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator
 from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
-from litellm.types.llms.openai import AllMessageValues, ChatCompletionUsageBlock
+from litellm.types.llms.openai import (
+    AllMessageValues,
+    ChatCompletionToolCallChunk,
+    ChatCompletionToolCallFunctionChunk,
+    ChatCompletionUsageBlock,
+)
 from litellm.types.utils import (
     Delta,
     GenericStreamingChunk,
@@ -99,6 +105,8 @@ class OllamaConfig(BaseConfig):
     system: Optional[str] = None
     template: Optional[str] = None
 
+    _prompted_tool_names: frozenset[str] = frozenset()
+
     def __init__(
         self,
         mirostat: Optional[int] = None,
@@ -376,6 +384,7 @@ def transform_request(
         format = optional_params.pop("format", None)
         images = optional_params.pop("images", None)
         think = optional_params.pop("think", None)
+        self._prompted_tool_names = _prompted_tool_names(optional_params.pop("prompted_tool_calls", None))
         data = {
             "model": model,
             "prompt": ollama_prompt,
@@ -439,14 +448,89 @@ def get_model_response_iterator(
             streaming_response=streaming_response,
             sync_stream=sync_stream,
             json_mode=json_mode,
+            tool_names=self._prompted_tool_names,
         )
 
 
+def _prompted_tool_names(prompted_tools: object) -> frozenset[str]:
+    """Names of the functions `get_optional_params` rewrote into this request's prompt."""
+    if not isinstance(prompted_tools, list):
+        return frozenset()
+
+    schemas = ((tool.get("function") or tool) for tool in prompted_tools if isinstance(tool, dict))
+    return frozenset(
+        schema["name"] for schema in schemas if isinstance(schema, dict) and isinstance(schema.get("name"), str)
+    )
+
+
+class _OllamaJsonModeToolCall(BaseModel):
+    """
+    The exact object litellm's own tool prompt asks ollama for, in `factory.function_call_prompt`.
+    Extra keys are rejected so a JSON body that merely happens to carry a `name` stays content.
+    """
+
+    model_config = ConfigDict(extra="forbid")
+
+    name: str
+    arguments: dict[str, object]
+
+
+def _build_tool_call(response_text: str, tool_names: frozenset[str]) -> ChatCompletionToolCallChunk | None:
+    """
+    Build a tool call out of a completed response body, but only for a function this request
+    actually offered. This is a stricter test than the one `OllamaConfig.transform_response`
+    applies to the same body, so it never converts anything that path would leave as content.
+    """
+    try:
+        function_call = _OllamaJsonModeToolCall.model_validate_json(response_text)
+    except ValidationError:
+        return None
+
+    if function_call.name not in tool_names:
+        return None
+
+    return ChatCompletionToolCallChunk(
+        id=f"call_{uuid.uuid4()}",
+        type="function",
+        function=ChatCompletionToolCallFunctionChunk(
+            name=function_call.name,
+            arguments=json.dumps(function_call.arguments),
+        ),
+        index=0,
+    )
+
+
 class OllamaTextCompletionResponseIterator(BaseModelResponseIterator):
-    def __init__(self, streaming_response, sync_stream: bool, json_mode: Optional[bool] = False):
+    def __init__(
+        self,
+        streaming_response,
+        sync_stream: bool,
+        json_mode: Optional[bool] = False,
+        tool_names: frozenset[str] = frozenset(),
+    ):
         super().__init__(streaming_response, sync_stream, json_mode)
         self.started_reasoning_content: bool = False
         self.finished_reasoning_content: bool = False
+        self.tool_names: frozenset[str] = tool_names
+        self.tool_call_buffer: str | None = "" if tool_names else None
+
+    def _hold_back_or_release(self, content: str) -> str | None:
+        """
+        `/api/generate` returns a tool call as a JSON object inside the response text, so it can
+        only be recognised once that object is complete. Hold text back while it can still turn
+        out to be one, and release everything held as soon as it cannot.
+        """
+        if self.tool_call_buffer is None:
+            return content
+
+        self.tool_call_buffer += content
+        stripped = self.tool_call_buffer.lstrip()
+        if not stripped or stripped.startswith("{"):
+            return None
+
+        released = self.tool_call_buffer
+        self.tool_call_buffer = None
+        return released
 
     def _handle_string_chunk(self, str_line: str) -> Union[GenericStreamingChunk, ModelResponseStream]:
         return self.chunk_parser(json.loads(str_line))
@@ -473,8 +557,21 @@ def chunk_parser(self, chunk: dict) -> Union[GenericStreamingChunk, ModelRespons
                         completion_tokens=eval_count,
                         total_tokens=prompt_eval_count + eval_count,
                     )
+
+                held_back = self.tool_call_buffer
+                self.tool_call_buffer = None
+                tool_call = _build_tool_call(held_back, self.tool_names) if held_back else None
+                if tool_call is not None:
+                    return GenericStreamingChunk(
+                        text="",
+                        tool_use=tool_call,
+                        is_finished=True,
+                        finish_reason="tool_calls",
+                        usage=usage,
+                    )
+
                 return GenericStreamingChunk(
-                    text=text,
+                    text=held_back or text,
                     is_finished=is_finished,
                     finish_reason=finish_reason,
                     usage=usage,
@@ -494,7 +591,7 @@ def chunk_parser(self, chunk: dict) -> Union[GenericStreamingChunk, ModelRespons
                     if self.started_reasoning_content and not self.finished_reasoning_content:
                         reasoning_content = text
                     else:
-                        content = text
+                        content = self._hold_back_or_release(text)
 
                 return ModelResponseStream(
                     choices=[
diff --git a/litellm/utils.py b/litellm/utils.py
index a11c5500503..3403a40168b 100644
--- a/litellm/utils.py
+++ b/litellm/utils.py
@@ -3746,6 +3746,7 @@ def pre_process_optional_params(passed_params: dict, non_default_params: dict, c
                     non_default_params.pop("tool_choice", None)  # causes ollama requests to hang
                 elif "functions" in non_default_params:
                     optional_params["functions_unsupported_model"] = non_default_params.pop("functions")
+                optional_params["prompted_tool_calls"] = optional_params.get("functions_unsupported_model") or []
             elif litellm.add_function_to_prompt:  # if user opts to add it to prompt instead
                 optional_params["functions_unsupported_model"] = non_default_params.pop(
                     "tools", non_default_params.pop("functions", None)
diff --git a/tests/test_litellm/llms/ollama/test_ollama_completion_transformation.py b/tests/test_litellm/llms/ollama/test_ollama_completion_transformation.py
index dd59cdcac1c..38edb1a704b 100644
--- a/tests/test_litellm/llms/ollama/test_ollama_completion_transformation.py
+++ b/tests/test_litellm/llms/ollama/test_ollama_completion_transformation.py
@@ -507,3 +507,320 @@ def test_chunk_parser_done_chunk(self):
         assert result["usage"]["prompt_tokens"] == 10
         assert result["usage"]["completion_tokens"] == 5
         assert result["usage"]["total_tokens"] == 15
+
+    def test_chunk_parser_streams_tool_call_instead_of_raw_json(self):
+        """A format=json tool call must arrive as tool_calls, not as JSON in the content."""
+        iterator = OllamaTextCompletionResponseIterator(
+            streaming_response=iter([]),
+            sync_stream=True,
+            json_mode=False,
+            tool_names=frozenset({"lookup_account", "lookup"}),
+        )
+
+        fragments = [
+            '{"',
+            "name",
+            '": "',
+            "lookup_account",
+            '", "',
+            "arguments",
+            '": {"',
+            "email",
+            '": "',
+            "maya.iyer@example.com",
+            '"}}',
+        ]
+        streamed_content = ""
+        for fragment in fragments:
+            result = iterator.chunk_parser({"model": "qwen2.5:3b", "response": fragment, "done": False})
+            assert isinstance(result, ModelResponseStream)
+            assert result.choices and result.choices[0].delta is not None
+            streamed_content += result.choices[0].delta.content or ""
+
+        assert streamed_content == ""
+
+        result = iterator.chunk_parser(
+            {
+                "model": "qwen2.5:3b",
+                "response": "",
+                "done": True,
+                "prompt_eval_count": 120,
+                "eval_count": 24,
+            }
+        )
+
+        assert result["text"] == ""
+        assert result["finish_reason"] == "tool_calls"
+        assert result["is_finished"] is True
+        assert result["tool_use"] is not None
+        assert result["tool_use"]["type"] == "function"
+        assert result["tool_use"]["index"] == 0
+        assert result["tool_use"]["id"]
+        assert result["tool_use"]["function"]["name"] == "lookup_account"
+        assert json.loads(result["tool_use"]["function"]["arguments"]) == {"email": "maya.iyer@example.com"}
+
+    def test_chunk_parser_streams_prose_incrementally(self):
+        """Prose must keep streaming fragment by fragment and carry no tool call."""
+        iterator = OllamaTextCompletionResponseIterator(
+            streaming_response=iter([]),
+            sync_stream=True,
+            json_mode=False,
+            tool_names=frozenset({"lookup_account", "lookup"}),
+        )
+
+        fragments = ["I ", "will ", "look ", "that ", "up", "."]
+        deltas = []
+        for fragment in fragments:
+            result = iterator.chunk_parser({"model": "qwen2.5:3b", "response": fragment, "done": False})
+            assert isinstance(result, ModelResponseStream)
+            assert result.choices and result.choices[0].delta is not None
+            deltas.append(result.choices[0].delta.content)
+
+        assert deltas == fragments
+
+        result = iterator.chunk_parser(
+            {
+                "model": "qwen2.5:3b",
+                "response": "",
+                "done": True,
+                "prompt_eval_count": 10,
+                "eval_count": 6,
+            }
+        )
+
+        assert result["text"] == ""
+        assert result["finish_reason"] == "stop"
+        assert result.get("tool_use") is None
+
+    def test_chunk_parser_releases_non_tool_call_json_as_content(self):
+        """A JSON body that is not a tool call must still be delivered as content."""
+        iterator = OllamaTextCompletionResponseIterator(
+            streaming_response=iter([]),
+            sync_stream=True,
+            json_mode=False,
+            tool_names=frozenset({"lookup_account", "lookup"}),
+        )
+
+        fragments = ['{"', "city", '": "', "Chennai", '"}']
+        streamed_content = ""
+        for fragment in fragments:
+            result = iterator.chunk_parser({"model": "qwen2.5:3b", "response": fragment, "done": False})
+            assert isinstance(result, ModelResponseStream)
+            assert result.choices and result.choices[0].delta is not None
+            streamed_content += result.choices[0].delta.content or ""
+
+        result = iterator.chunk_parser(
+            {
+                "model": "qwen2.5:3b",
+                "response": "",
+                "done": True,
+                "prompt_eval_count": 10,
+                "eval_count": 6,
+            }
+        )
+
+        assert streamed_content + result["text"] == '{"city": "Chennai"}'
+        assert result["finish_reason"] == "stop"
+        assert result.get("tool_use") is None
+
+    def test_chunk_parser_leaves_json_carrying_extra_keys_as_content(self):
+        """A JSON body that merely happens to carry a name must not be mistaken for a tool call."""
+        iterator = OllamaTextCompletionResponseIterator(
+            streaming_response=iter([]),
+            sync_stream=True,
+            json_mode=False,
+            tool_names=frozenset({"lookup_account", "lookup"}),
+        )
+
+        body = '{"name": "Chennai", "arguments": {"x": 1}, "population": 7000000}'
+        streamed_content = ""
+        for fragment in [body[i : i + 6] for i in range(0, len(body), 6)]:
+            result = iterator.chunk_parser({"model": "qwen2.5:3b", "response": fragment, "done": False})
+            assert isinstance(result, ModelResponseStream)
+            assert result.choices and result.choices[0].delta is not None
+            streamed_content += result.choices[0].delta.content or ""
+
+        result = iterator.chunk_parser(
+            {"model": "qwen2.5:3b", "response": "", "done": True, "prompt_eval_count": 5, "eval_count": 9}
+        )
+
+        assert streamed_content + result["text"] == body
+        assert result["finish_reason"] == "stop"
+        assert result.get("tool_use") is None
+
+    def test_chunk_parser_leaves_non_object_arguments_as_content(self):
+        """`arguments` must be the object the tool prompt asks for, not any value that happens to be there."""
+        iterator = OllamaTextCompletionResponseIterator(
+            streaming_response=iter([]),
+            sync_stream=True,
+            json_mode=False,
+            tool_names=frozenset({"lookup_account", "lookup"}),
+        )
+
+        body = '{"name": "lookup", "arguments": "not an object"}'
+        streamed_content = ""
+        for fragment in [body[i : i + 6] for i in range(0, len(body), 6)]:
+            result = iterator.chunk_parser({"model": "qwen2.5:3b", "response": fragment, "done": False})
+            assert isinstance(result, ModelResponseStream)
+            assert result.choices and result.choices[0].delta is not None
+            streamed_content += result.choices[0].delta.content or ""
+
+        result = iterator.chunk_parser(
+            {"model": "qwen2.5:3b", "response": "", "done": True, "prompt_eval_count": 5, "eval_count": 9}
+        )
+
+        assert streamed_content + result["text"] == body
+        assert result["finish_reason"] == "stop"
+        assert result.get("tool_use") is None
+
+
+TOOL_CALL_BODY = '{"name": "lookup_account", "arguments": {"email": "maya.iyer@example.com"}}'
+
+LOOKUP_ACCOUNT_TOOL = {
+    "type": "function",
+    "function": {
+        "name": "lookup_account",
+        "description": "Look up a customer account by email address",
+        "parameters": {"type": "object", "properties": {"email": {"type": "string"}}, "required": ["email"]},
+    },
+}
+
+
+def _generate_transport():
+    """Stands in for ollama's /api/generate, fragmenting the body the way it really does."""
+    import httpx
+
+    done = {"model": "qwen2.5:3b", "response": "", "done": True, "prompt_eval_count": 146, "eval_count": 21}
+
+    def handler(request: "httpx.Request") -> "httpx.Response":
+        fragments = [TOOL_CALL_BODY[i : i + 4] for i in range(0, len(TOOL_CALL_BODY), 4)]
+        lines = [json.dumps({"model": "qwen2.5:3b", "response": f, "done": False}) for f in fragments]
+        lines.append(json.dumps(done))
+        return httpx.Response(200, content=("\n".join(lines) + "\n").encode())
+
+    return httpx.MockTransport(handler)
+
+
+class TestOllamaStreamingToolCallsEndToEnd:
+    def test_streamed_tool_call_is_not_delivered_as_content(self):
+        """The reported bug: a streamed tool call reaching the caller as raw JSON in the content."""
+        import httpx
+
+        from litellm import completion
+        from litellm.llms.custom_httpx.http_handler import HTTPHandler
+
+        client = HTTPHandler(client=httpx.Client(transport=_generate_transport()))
+
+        response = completion(
+            model="ollama/qwen2.5:3b",
+            messages=[{"role": "user", "content": "Cancel the subscription for maya.iyer@example.com"}],
+            tools=[LOOKUP_ACCOUNT_TOOL],
+            api_base="http://localhost:11434",
+            stream=True,
+            client=client,
+        )
+
+        streamed_content = ""
+        tool_calls = []
+        finish_reason = None
+        for chunk in response:
+            delta = chunk.choices[0].delta
+            streamed_content += getattr(delta, "content", None) or ""
+            tool_calls += getattr(delta, "tool_calls", None) or []
+            finish_reason = chunk.choices[0].finish_reason or finish_reason
+
+        assert streamed_content == ""
+        assert finish_reason == "tool_calls"
+        assert len(tool_calls) == 1
+        assert tool_calls[0].function.name == "lookup_account"
+        assert json.loads(tool_calls[0].function.arguments) == {"email": "maya.iyer@example.com"}
+
+    def test_detection_is_off_for_a_request_that_offered_no_tools(self):
+        """No tools were rewritten into a prompt, so a tool-shaped body is just content."""
+        iterator = OllamaTextCompletionResponseIterator(streaming_response=iter([]), sync_stream=True, json_mode=False)
+
+        streamed_content = ""
+        for fragment in [TOOL_CALL_BODY[i : i + 6] for i in range(0, len(TOOL_CALL_BODY), 6)]:
+            result = iterator.chunk_parser({"model": "qwen2.5:3b", "response": fragment, "done": False})
+            assert isinstance(result, ModelResponseStream)
+            assert result.choices and result.choices[0].delta is not None
+            streamed_content += result.choices[0].delta.content or ""
+
+        result = iterator.chunk_parser(
+            {"model": "qwen2.5:3b", "response": "", "done": True, "prompt_eval_count": 5, "eval_count": 9}
+        )
+
+        assert streamed_content == TOOL_CALL_BODY
+        assert result["finish_reason"] == "stop"
+        assert result.get("tool_use") is None
+
+    def test_detection_is_armed_only_with_the_functions_the_request_offered(self):
+        """`get_optional_params` passes on the functions it rewrote; those are the only ones accepted."""
+        from litellm.utils import get_optional_params
+
+        config = OllamaConfig()
+        base = {
+            "model": "qwen2.5:3b",
+            "messages": [{"role": "user", "content": "hi"}],
+            "litellm_params": {},
+            "headers": {},
+        }
+
+        json_mode_only = get_optional_params(
+            model="qwen2.5:3b",
+            custom_llm_provider="ollama",
+            stream=True,
+            response_format={"type": "json_object"},
+        )
+        assert "prompted_tool_calls" not in json_mode_only
+        config.transform_request(optional_params=json_mode_only, **base)
+        assert config.get_model_response_iterator(iter([]), sync_stream=True).tool_names == frozenset()
+
+        with_tools = get_optional_params(
+            model="qwen2.5:3b",
+            custom_llm_provider="ollama",
+            stream=True,
+            tools=[LOOKUP_ACCOUNT_TOOL],
+        )
+        assert with_tools["prompted_tool_calls"] == [LOOKUP_ACCOUNT_TOOL]
+        config.transform_request(optional_params=with_tools, **base)
+        assert config.get_model_response_iterator(iter([]), sync_stream=True).tool_names == frozenset(
+            {"lookup_account"}
+        )
+
+    def test_a_function_the_request_never_offered_stays_content(self):
+        """A body naming some other function must not be synthesised into a tool call."""
+        iterator = OllamaTextCompletionResponseIterator(
+            streaming_response=iter([]), sync_stream=True, json_mode=False, tool_names=frozenset({"lookup_account"})
+        )
+
+        body = '{"name": "delete_account", "arguments": {"email": "maya.iyer@example.com"}}'
+        streamed_content = ""
+        for fragment in [body[i : i + 6] for i in range(0, len(body), 6)]:
+            result = iterator.chunk_parser({"model": "qwen2.5:3b", "response": fragment, "done": False})
+            assert isinstance(result, ModelResponseStream)
+            assert result.choices and result.choices[0].delta is not None
+            streamed_content += result.choices[0].delta.content or ""
+
+        result = iterator.chunk_parser(
+            {"model": "qwen2.5:3b", "response": "", "done": True, "prompt_eval_count": 5, "eval_count": 9}
+        )
+
+        assert streamed_content + result["text"] == body
+        assert result["finish_reason"] == "stop"
+        assert result.get("tool_use") is None
+
+    def test_prompted_tool_calls_marker_is_not_sent_to_ollama(self):
+        """The marker is litellm-internal; it must not leak into the request body."""
+        config = OllamaConfig()
+
+        data = config.transform_request(
+            model="qwen2.5:3b",
+            messages=[{"role": "user", "content": "hi"}],
+            optional_params={"stream": True, "format": "json", "prompted_tool_calls": True},
+            litellm_params={},
+            headers={},
+        )
+
+        assert "prompted_tool_calls" not in data
+        assert "prompted_tool_calls" not in data["options"]