remove conflicting default for tool prompt format in chat completion (#742)

# What does this PR do?
We are setting a default value of json for tool prompt format, which
conflicts with llama 3.2/3.3 models since they use python list. This PR
changes the defaults to None and in the code, we infer default based on
the model.

Addresses: #695 

Tests:
❯ LLAMA_STACK_BASE_URL=http://localhost:5000 pytest -v
tests/client-sdk/inference/test_inference.py -k
"test_text_chat_completion"

 pytest llama_stack/providers/tests/inference/test_prompt_adapter.py

docs/resources/llama-stack-spec.html

@@ -4503,10 +4503,6 @@
                                 }
                             ]
                         }
-                    },
-                    "tool_prompt_format": {
-                        "$ref": "#/components/schemas/ToolPromptFormat",
-                        "default": "json"
                     }
                 },
                 "additionalProperties": false,
@@ -6522,10 +6518,6 @@
                                 }
                             ]
                         }
-                    },
-                    "tool_prompt_format": {
-                        "$ref": "#/components/schemas/ToolPromptFormat",
-                        "default": "json"
                     }
                 },
                 "additionalProperties": false,

docs/resources/llama-stack-spec.yaml

@@ -2600,9 +2600,6 @@ components:
           type: string
         tool_host:
           $ref: '#/components/schemas/ToolHost'
-        tool_prompt_format:
-          $ref: '#/components/schemas/ToolPromptFormat'
-          default: json
         toolgroup_id:
           type: string
         type:
@@ -2704,9 +2701,6 @@ components:
           items:
             $ref: '#/components/schemas/ToolParameter'
           type: array
-        tool_prompt_format:
-          $ref: '#/components/schemas/ToolPromptFormat'
-          default: json
       required:
       - name
       type: object

llama_stack/apis/batch_inference/batch_inference.py

@@ -7,7 +7,6 @@
 from typing import List, Optional, Protocol, runtime_checkable
 
 from llama_models.schema_utils import json_schema_type, webmethod
-
 from pydantic import BaseModel, Field
 
 from llama_stack.apis.inference import (
@@ -44,9 +43,7 @@ class BatchChatCompletionRequest(BaseModel):
     # zero-shot tool definitions as input to the model
     tools: Optional[List[ToolDefinition]] = Field(default_factory=list)
     tool_choice: Optional[ToolChoice] = Field(default=ToolChoice.auto)
-    tool_prompt_format: Optional[ToolPromptFormat] = Field(
+    tool_prompt_format: Optional[ToolPromptFormat] = Field(default=None)
-        default=ToolPromptFormat.json
-    )
     logprobs: Optional[LogProbConfig] = None
 
 
@@ -75,6 +72,6 @@ class BatchInference(Protocol):
         # zero-shot tool definitions as input to the model
         tools: Optional[List[ToolDefinition]] = list,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
-        tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
+        tool_prompt_format: Optional[ToolPromptFormat] = None,
         logprobs: Optional[LogProbConfig] = None,
     ) -> BatchChatCompletionResponse: ...

llama_stack/apis/inference/inference.py

@@ -5,7 +5,6 @@
 # the root directory of this source tree.
 
 from enum import Enum
-
 from typing import (
     Any,
     AsyncIterator,
@@ -26,16 +25,12 @@ from llama_models.llama3.api.datatypes import (
     ToolDefinition,
     ToolPromptFormat,
 )
-
 from llama_models.schema_utils import json_schema_type, register_schema, webmethod
-
 from pydantic import BaseModel, Field, field_validator
 from typing_extensions import Annotated
 
 from llama_stack.apis.common.content_types import InterleavedContent
-
 from llama_stack.apis.models import Model
-
 from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol
 
 
@@ -256,9 +251,7 @@ class ChatCompletionRequest(BaseModel):
     # zero-shot tool definitions as input to the model
     tools: Optional[List[ToolDefinition]] = Field(default_factory=list)
     tool_choice: Optional[ToolChoice] = Field(default=ToolChoice.auto)
-    tool_prompt_format: Optional[ToolPromptFormat] = Field(
+    tool_prompt_format: Optional[ToolPromptFormat] = Field(default=None)
-        default=ToolPromptFormat.json
-    )
     response_format: Optional[ResponseFormat] = None
 
     stream: Optional[bool] = False
@@ -289,9 +282,7 @@ class BatchChatCompletionRequest(BaseModel):
     # zero-shot tool definitions as input to the model
     tools: Optional[List[ToolDefinition]] = Field(default_factory=list)
     tool_choice: Optional[ToolChoice] = Field(default=ToolChoice.auto)
-    tool_prompt_format: Optional[ToolPromptFormat] = Field(
+    tool_prompt_format: Optional[ToolPromptFormat] = Field(default=None)
-        default=ToolPromptFormat.json
-    )
     logprobs: Optional[LogProbConfig] = None
 
 
@@ -334,7 +325,7 @@ class Inference(Protocol):
         # zero-shot tool definitions as input to the model
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
-        tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
+        tool_prompt_format: Optional[ToolPromptFormat] = None,
         response_format: Optional[ResponseFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,

llama_stack/apis/tools/tools.py

@@ -7,7 +7,6 @@
 from enum import Enum
 from typing import Any, Dict, List, Literal, Optional
 
-from llama_models.llama3.api.datatypes import ToolPromptFormat
 from llama_models.schema_utils import json_schema_type, webmethod
 from pydantic import BaseModel, Field
 from typing_extensions import Protocol, runtime_checkable
@@ -41,9 +40,6 @@ class Tool(Resource):
     description: str
     parameters: List[ToolParameter]
     metadata: Optional[Dict[str, Any]] = None
-    tool_prompt_format: Optional[ToolPromptFormat] = Field(
-        default=ToolPromptFormat.json
-    )
 
 
 @json_schema_type
@@ -52,9 +48,6 @@ class ToolDef(BaseModel):
     description: Optional[str] = None
     parameters: Optional[List[ToolParameter]] = None
     metadata: Optional[Dict[str, Any]] = None
-    tool_prompt_format: Optional[ToolPromptFormat] = Field(
-        default=ToolPromptFormat.json
-    )
 
 
 @json_schema_type

llama_stack/distribution/routers/routers.py

@@ -127,7 +127,7 @@ class InferenceRouter(Inference):
         response_format: Optional[ResponseFormat] = None,
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
-        tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
+        tool_prompt_format: Optional[ToolPromptFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
     ) -> AsyncGenerator:

llama_stack/distribution/routers/routing_tables.py

@@ -523,7 +523,6 @@ class ToolGroupsRoutingTable(CommonRoutingTableImpl, ToolGroups):
                     description=tool_def.description or "",
                     parameters=tool_def.parameters or [],
                     provider_id=provider_id,
-                    tool_prompt_format=tool_def.tool_prompt_format,
                     provider_resource_id=tool_def.name,
                     metadata=tool_def.metadata,
                     tool_host=tool_host,

llama_stack/providers/inline/inference/meta_reference/inference.py

@@ -6,7 +6,6 @@
 
 import asyncio
 import logging
-
 from typing import AsyncGenerator, List, Optional, Union
 
 from llama_models.llama3.api.datatypes import (
@@ -37,7 +36,6 @@ from llama_stack.apis.inference import (
     ToolCallParseStatus,
     ToolChoice,
 )
-
 from llama_stack.apis.models import Model, ModelType
 from llama_stack.providers.datatypes import ModelsProtocolPrivate
 from llama_stack.providers.utils.inference.embedding_mixin import (
@@ -262,7 +260,7 @@ class MetaReferenceInferenceImpl(
         response_format: Optional[ResponseFormat] = None,
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
-        tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
+        tool_prompt_format: Optional[ToolPromptFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
     ) -> AsyncGenerator:

llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers.py

@@ -22,6 +22,7 @@ from llama_stack.providers.datatypes import Model, ModelsProtocolPrivate
 from llama_stack.providers.utils.inference.embedding_mixin import (
     SentenceTransformerEmbeddingMixin,
 )
+
 from .config import SentenceTransformersInferenceConfig
 
 log = logging.getLogger(__name__)
@@ -67,7 +68,7 @@ class SentenceTransformersInferenceImpl(
         response_format: Optional[ResponseFormat] = None,
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
-        tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
+        tool_prompt_format: Optional[ToolPromptFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
     ) -> AsyncGenerator:

llama_stack/providers/inline/inference/vllm/vllm.py

@@ -10,10 +10,8 @@ import uuid
 from typing import AsyncGenerator, List, Optional
 
 from llama_models.llama3.api.chat_format import ChatFormat
-
 from llama_models.llama3.api.tokenizer import Tokenizer
 from llama_models.sku_list import resolve_model
-
 from vllm.engine.arg_utils import AsyncEngineArgs
 from vllm.engine.async_llm_engine import AsyncLLMEngine
 from vllm.sampling_params import SamplingParams as VLLMSamplingParams
@@ -36,7 +34,6 @@ from llama_stack.apis.inference import (
     ToolPromptFormat,
 )
 from llama_stack.apis.models import Model
-
 from llama_stack.providers.datatypes import ModelsProtocolPrivate
 from llama_stack.providers.utils.inference.openai_compat import (
     OpenAICompatCompletionChoice,
@@ -50,7 +47,6 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
 
 from .config import VLLMConfig
 
-
 log = logging.getLogger(__name__)
 
 
@@ -146,7 +142,7 @@ class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
         sampling_params: Optional[SamplingParams] = SamplingParams(),
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
-        tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
+        tool_prompt_format: Optional[ToolPromptFormat] = None,
         response_format: Optional[ResponseFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,

llama_stack/providers/remote/inference/bedrock/bedrock.py

@@ -10,7 +10,6 @@ from typing import AsyncGenerator, AsyncIterator, Dict, List, Optional, Union
 from botocore.client import BaseClient
 from llama_models.datatypes import CoreModelId
 from llama_models.llama3.api.chat_format import ChatFormat
-
 from llama_models.llama3.api.tokenizer import Tokenizer
 
 from llama_stack.apis.common.content_types import InterleavedContent
@@ -30,7 +29,6 @@ from llama_stack.apis.inference import (
 )
 from llama_stack.providers.remote.inference.bedrock.config import BedrockConfig
 from llama_stack.providers.utils.bedrock.client import create_bedrock_client
-
 from llama_stack.providers.utils.inference.model_registry import (
     build_model_alias,
     ModelRegistryHelper,
@@ -47,7 +45,6 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
     interleaved_content_as_str,
 )
 
-
 MODEL_ALIASES = [
     build_model_alias(
         "meta.llama3-1-8b-instruct-v1:0",
@@ -101,7 +98,7 @@ class BedrockInferenceAdapter(ModelRegistryHelper, Inference):
         response_format: Optional[ResponseFormat] = None,
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
-        tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
+        tool_prompt_format: Optional[ToolPromptFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
     ) -> Union[

llama_stack/providers/remote/inference/cerebras/cerebras.py

@@ -7,11 +7,8 @@
 from typing import AsyncGenerator, List, Optional, Union
 
 from cerebras.cloud.sdk import AsyncCerebras
-
 from llama_models.datatypes import CoreModelId
-
 from llama_models.llama3.api.chat_format import ChatFormat
-
 from llama_models.llama3.api.tokenizer import Tokenizer
 
 from llama_stack.apis.common.content_types import InterleavedContent
@@ -29,7 +26,6 @@ from llama_stack.apis.inference import (
     ToolDefinition,
     ToolPromptFormat,
 )
-
 from llama_stack.providers.utils.inference.model_registry import (
     build_model_alias,
     ModelRegistryHelper,
@@ -48,7 +44,6 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
 
 from .config import CerebrasImplConfig
 
-
 model_aliases = [
     build_model_alias(
         "llama3.1-8b",
@@ -130,7 +125,7 @@ class CerebrasInferenceAdapter(ModelRegistryHelper, Inference):
         sampling_params: Optional[SamplingParams] = SamplingParams(),
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
-        tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
+        tool_prompt_format: Optional[ToolPromptFormat] = None,
         response_format: Optional[ResponseFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,

llama_stack/providers/remote/inference/databricks/databricks.py

@@ -7,11 +7,8 @@
 from typing import AsyncGenerator, List, Optional
 
 from llama_models.datatypes import CoreModelId
-
 from llama_models.llama3.api.chat_format import ChatFormat
-
 from llama_models.llama3.api.tokenizer import Tokenizer
-
 from openai import OpenAI
 
 from llama_stack.apis.common.content_types import InterleavedContent
@@ -28,7 +25,6 @@ from llama_stack.apis.inference import (
     ToolDefinition,
     ToolPromptFormat,
 )
-
 from llama_stack.providers.utils.inference.model_registry import (
     build_model_alias,
     ModelRegistryHelper,
@@ -44,7 +40,6 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
 
 from .config import DatabricksImplConfig
 
-
 model_aliases = [
     build_model_alias(
         "databricks-meta-llama-3-1-70b-instruct",
@@ -91,7 +86,7 @@ class DatabricksInferenceAdapter(ModelRegistryHelper, Inference):
         response_format: Optional[ResponseFormat] = None,
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
-        tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
+        tool_prompt_format: Optional[ToolPromptFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
     ) -> AsyncGenerator:

llama_stack/providers/remote/inference/fireworks/fireworks.py

@@ -8,7 +8,6 @@ from typing import AsyncGenerator, List, Optional, Union
 
 from fireworks.client import Fireworks
 from llama_models.datatypes import CoreModelId
-
 from llama_models.llama3.api.chat_format import ChatFormat
 from llama_models.llama3.api.tokenizer import Tokenizer
 
@@ -52,7 +51,6 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
 
 from .config import FireworksImplConfig
 
-
 MODEL_ALIASES = [
     build_model_alias(
         "fireworks/llama-v3p1-8b-instruct",
@@ -198,7 +196,7 @@ class FireworksInferenceAdapter(
         sampling_params: Optional[SamplingParams] = SamplingParams(),
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
-        tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
+        tool_prompt_format: Optional[ToolPromptFormat] = None,
         response_format: Optional[ResponseFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,

llama_stack/providers/remote/inference/groq/groq.py

@@ -33,6 +33,7 @@ from llama_stack.providers.utils.inference.model_registry import (
     build_model_alias_with_just_provider_model_id,
     ModelRegistryHelper,
 )
+
 from .groq_utils import (
     convert_chat_completion_request,
     convert_chat_completion_response,
@@ -94,9 +95,7 @@ class GroqInferenceAdapter(Inference, ModelRegistryHelper, NeedsRequestProviderD
         response_format: Optional[ResponseFormat] = None,
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
-        tool_prompt_format: Optional[
+        tool_prompt_format: Optional[ToolPromptFormat] = None,
-            ToolPromptFormat
-        ] = None,  # API default is ToolPromptFormat.json, we default to None to detect user input
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
     ) -> Union[

llama_stack/providers/remote/inference/nvidia/nvidia.py

@@ -175,9 +175,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
         response_format: Optional[ResponseFormat] = None,
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
-        tool_prompt_format: Optional[
+        tool_prompt_format: Optional[ToolPromptFormat] = None,
-            ToolPromptFormat
-        ] = None,  # API default is ToolPromptFormat.json, we default to None to detect user input
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
     ) -> Union[

llama_stack/providers/remote/inference/ollama/ollama.py

@@ -9,7 +9,6 @@ from typing import AsyncGenerator, List, Optional, Union
 
 import httpx
 from llama_models.datatypes import CoreModelId
-
 from llama_models.llama3.api.chat_format import ChatFormat
 from llama_models.llama3.api.tokenizer import Tokenizer
 from ollama import AsyncClient
@@ -35,7 +34,6 @@ from llama_stack.apis.inference import (
 )
 from llama_stack.apis.models import Model, ModelType
 from llama_stack.providers.datatypes import ModelsProtocolPrivate
-
 from llama_stack.providers.utils.inference.model_registry import (
     build_model_alias,
     build_model_alias_with_just_provider_model_id,
@@ -222,7 +220,7 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
         response_format: Optional[ResponseFormat] = None,
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
-        tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
+        tool_prompt_format: Optional[ToolPromptFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
     ) -> AsyncGenerator:

llama_stack/providers/remote/inference/tgi/tgi.py

@@ -30,13 +30,11 @@ from llama_stack.apis.inference import (
     ToolPromptFormat,
 )
 from llama_stack.apis.models import Model
-
 from llama_stack.providers.datatypes import ModelsProtocolPrivate
 from llama_stack.providers.utils.inference.model_registry import (
     build_model_alias,
     ModelRegistryHelper,
 )
-
 from llama_stack.providers.utils.inference.openai_compat import (
     get_sampling_options,
     OpenAICompatCompletionChoice,
@@ -205,7 +203,7 @@ class _HfAdapter(Inference, ModelsProtocolPrivate):
         sampling_params: Optional[SamplingParams] = SamplingParams(),
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
-        tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
+        tool_prompt_format: Optional[ToolPromptFormat] = None,
         response_format: Optional[ResponseFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,

llama_stack/providers/remote/inference/together/together.py

@@ -184,7 +184,7 @@ class TogetherInferenceAdapter(
         sampling_params: Optional[SamplingParams] = SamplingParams(),
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
-        tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
+        tool_prompt_format: Optional[ToolPromptFormat] = None,
         response_format: Optional[ResponseFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,

llama_stack/providers/remote/inference/vllm/vllm.py

@@ -10,7 +10,6 @@ from typing import AsyncGenerator, List, Optional, Union
 from llama_models.llama3.api.chat_format import ChatFormat
 from llama_models.llama3.api.tokenizer import Tokenizer
 from llama_models.sku_list import all_registered_models
-
 from openai import OpenAI
 
 from llama_stack.apis.common.content_types import InterleavedContent
@@ -33,7 +32,6 @@ from llama_stack.apis.inference import (
 )
 from llama_stack.apis.models import Model, ModelType
 from llama_stack.providers.datatypes import ModelsProtocolPrivate
-
 from llama_stack.providers.utils.inference.model_registry import (
     build_model_alias,
     ModelRegistryHelper,
@@ -54,7 +52,6 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
 
 from .config import VLLMInferenceAdapterConfig
 
-
 log = logging.getLogger(__name__)
 
 
@@ -105,7 +102,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
         response_format: Optional[ResponseFormat] = None,
         tools: Optional[List[ToolDefinition]] = None,
         tool_choice: Optional[ToolChoice] = ToolChoice.auto,
-        tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
+        tool_prompt_format: Optional[ToolPromptFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
     ) -> AsyncGenerator:

llama_stack/providers/utils/inference/prompt_adapter.py

@@ -358,14 +358,13 @@ def augment_messages_for_tools_llama_3_1(
 
     has_custom_tools = any(isinstance(dfn.tool_name, str) for dfn in request.tools)
     if has_custom_tools:
-        if request.tool_prompt_format == ToolPromptFormat.json:
+        fmt = request.tool_prompt_format or ToolPromptFormat.json
+        if fmt == ToolPromptFormat.json:
             tool_gen = JsonCustomToolGenerator()
-        elif request.tool_prompt_format == ToolPromptFormat.function_tag:
+        elif fmt == ToolPromptFormat.function_tag:
             tool_gen = FunctionTagCustomToolGenerator()
         else:
-            raise ValueError(
+            raise ValueError(f"Non supported ToolPromptFormat {fmt}")
-                f"Non supported ToolPromptFormat {request.tool_prompt_format}"
-            )
 
         custom_tools = [t for t in request.tools if isinstance(t.tool_name, str)]
         custom_template = tool_gen.gen(custom_tools)
@@ -410,7 +409,8 @@ def augment_messages_for_tools_llama_3_2(
 
     custom_tools = [dfn for dfn in request.tools if isinstance(dfn.tool_name, str)]
     if custom_tools:
-        if request.tool_prompt_format != ToolPromptFormat.python_list:
+        fmt = request.tool_prompt_format or ToolPromptFormat.python_list
+        if fmt != ToolPromptFormat.python_list:
             raise ValueError(
                 f"Non supported ToolPromptFormat {request.tool_prompt_format}"
             )