regen cassette

* Added a new utility function to remove OpenAI-specific fields from schemas, ensuring compatibility with Gemini and Bedrock APIs.
* Updated BedrockCompletion and GeminiCompletion classes to utilize this function for cleaning parameters before API calls.
* Introduced tests to validate the functionality of the new utility, ensuring it correctly strips incompatible fields while preserving required properties.

lib/crewai/src/crewai/llms/providers/bedrock/completion.py

@@ -1948,6 +1948,9 @@ class BedrockCompletion(BaseLLM):
     ) -> list[ConverseToolTypeDef]:
         """Convert CrewAI tools to Converse API format following AWS specification."""
         from crewai.llms.providers.utils.common import safe_tool_conversion
+        from crewai.utilities.pydantic_schema_utils import (
+            strip_openai_specific_schema_fields,
+        )
 
         converse_tools: list[ConverseToolTypeDef] = []
 
@@ -1961,6 +1964,7 @@ class BedrockCompletion(BaseLLM):
                 }
 
                 if parameters and isinstance(parameters, dict):
+                    parameters = strip_openai_specific_schema_fields(parameters)
                     input_schema: ToolInputSchema = {"json": parameters}
                     tool_spec["inputSchema"] = input_schema
 

lib/crewai/src/crewai/llms/providers/gemini/completion.py

@@ -529,10 +529,16 @@ class GeminiCompletion(BaseLLM):
         gemini_tools = []
 
         from crewai.llms.providers.utils.common import safe_tool_conversion
+        from crewai.utilities.pydantic_schema_utils import (
+            strip_openai_specific_schema_fields,
+        )
 
         for tool in tools:
             name, description, parameters = safe_tool_conversion(tool, "Gemini")
 
+            if parameters:
+                parameters = strip_openai_specific_schema_fields(parameters)
+
             function_declaration = types.FunctionDeclaration(
                 name=name,
                 description=description,

lib/crewai/src/crewai/utilities/agent_utils.py

@@ -168,7 +168,9 @@ def convert_tools_to_openai_schema(
         parameters: dict[str, Any] = {}
         if hasattr(tool, "args_schema") and tool.args_schema is not None:
             try:
-                schema_output = generate_model_description(tool.args_schema)
+                schema_output = generate_model_description(
+                    tool.args_schema, strip_null_types=False
+                )
                 parameters = schema_output.get("json_schema", {}).get("schema", {})
                 # Remove title and description from schema root as they're redundant
                 parameters.pop("title", None)

lib/crewai/src/crewai/utilities/pydantic_schema_utils.py

@@ -417,7 +417,40 @@ def strip_null_from_types(schema: dict[str, Any]) -> dict[str, Any]:
     return schema
 
 
-def generate_model_description(model: type[BaseModel]) -> ModelDescription:
+def strip_openai_specific_schema_fields(schema: dict[str, Any]) -> dict[str, Any]:
+    """Remove OpenAI strict-mode fields that are incompatible with other providers.
+
+    OpenAI strict mode requires ``additionalProperties: false`` on every object
+    and all properties listed in ``required``.  Gemini and Bedrock reject these
+    fields with schema validation errors, so we strip them before handing off to
+    non-OpenAI providers.
+
+    Specifically removes (recursively):
+    - ``additionalProperties`` — not supported by Gemini / Bedrock tool schemas
+
+    Args:
+        schema: JSON schema dictionary (mutated in place and returned).
+
+    Returns:
+        The modified schema.
+    """
+    if isinstance(schema, dict):
+        schema.pop("additionalProperties", None)
+        for value in schema.values():
+            if isinstance(value, dict):
+                strip_openai_specific_schema_fields(value)
+            elif isinstance(value, list):
+                for item in value:
+                    if isinstance(item, dict):
+                        strip_openai_specific_schema_fields(item)
+    return schema
+
+
+def generate_model_description(
+    model: type[BaseModel],
+    *,
+    strip_null_types: bool = True,
+) -> ModelDescription:
     """Generate JSON schema description of a Pydantic model.
 
     This function takes a Pydantic model class and returns its JSON schema,
@@ -426,6 +459,9 @@ def generate_model_description(model: type[BaseModel]) -> ModelDescription:
 
     Args:
         model: A Pydantic model class.
+        strip_null_types: When ``True`` (default), remove ``null`` from
+            ``anyOf`` / ``type`` arrays.  Set to ``False`` to allow sending ``null`` for
+            optional fields.
 
     Returns:
         A ModelDescription with JSON schema representation of the model.
@@ -442,7 +478,9 @@ def generate_model_description(model: type[BaseModel]) -> ModelDescription:
     json_schema = fix_discriminator_mappings(json_schema)
     json_schema = convert_oneof_to_anyof(json_schema)
     json_schema = ensure_all_properties_required(json_schema)
-    json_schema = strip_null_from_types(json_schema)
+
+    if strip_null_types:
+        json_schema = strip_null_from_types(json_schema)
 
     return {
         "type": "json_schema",

lib/crewai/tests/agents/test_native_tool_calling.py

@@ -559,6 +559,39 @@ class TestAnthropicNativeToolCalling:
         _assert_tools_overlapped()
 
 
+_MCP_OPTIONAL_FIELDS_TOOL_DEFS = [
+    {
+        "name": "filtered_search",
+        "description": (
+            "Search a knowledge base. Pass null for any filter you don't need."
+        ),
+        "inputSchema": {
+            "type": "object",
+            "properties": {
+                "query": {
+                    "type": "string",
+                    "description": "Search query string",
+                },
+                "filter_type": {
+                    "anyOf": [
+                        {"type": "string", "enum": ["news", "blog", "docs"]},
+                        {"type": "null"},
+                    ],
+                    "default": None,
+                    "description": "Optional content-type filter",
+                },
+                "page_id": {
+                    "anyOf": [{"type": "string"}, {"type": "null"}],
+                    "default": None,
+                    "description": "Optional page UUID",
+                },
+            },
+            "required": ["query"],
+        },
+    }
+]
+
+
 # =============================================================================
 # Google/Gemini Provider Tests
 # =============================================================================
@@ -796,6 +829,9 @@ class TestAzureNativeToolCalling:
         _assert_tools_overlapped()
 
 
+    
+
+
 # =============================================================================
 # Bedrock Provider Tests
 # =============================================================================
@@ -929,6 +965,135 @@ class TestNativeToolCallingBehavior:
         assert hasattr(llm, "supports_function_calling")
         assert llm.supports_function_calling() is True
 
+    @pytest.mark.vcr()
+    def test_gemini_mcp_tool_with_optional_fields_no_schema_error(self) -> None:
+        """MCP tools with Optional fields must not cause Gemini schema validation errors.
+
+        Regression test for https://github.com/crewAIInc/crewAI/issues/4472.
+
+        Before the fix, generate_model_description() added additionalProperties:false
+        to the tool schema. Gemini rejects this field, raising:
+          "value at properties.<field> must be a list"
+
+        With the fix, strip_openai_specific_schema_fields() removes it before the
+        schema is sent to the Gemini API.
+
+        To record the cassette:
+            RUN_LIVE_GEMINI_TESTS=true GOOGLE_API_KEY=<key> \\
+            uv run pytest tests/agents/test_native_tool_calling.py \\
+                ::TestGeminiNativeToolCalling::test_gemini_mcp_tool_with_optional_fields_no_schema_error \\
+                --record-mode=once -v
+        """
+        from unittest.mock import AsyncMock, patch
+
+        from crewai.mcp.config import MCPServerStdio
+
+        stdio_config = MCPServerStdio(command="python", args=["fake_mcp_server.py"])
+
+        agent = Agent(
+            role="Knowledge Researcher",
+            goal="Search a knowledge base and return relevant results",
+            backstory="You are a helpful researcher who uses the filtered_search tool.",
+            mcps=[stdio_config],
+            llm=LLM(model="gemini/gemini-2.5-flash"),
+            verbose=False,
+            max_iter=3,
+        )
+
+        with patch("crewai.agent.core.MCPClient") as mock_client_class:
+            mock_client = AsyncMock()
+            mock_client.list_tools = AsyncMock(
+                return_value=_MCP_OPTIONAL_FIELDS_TOOL_DEFS
+            )
+            mock_client.connected = False
+            mock_client.connect = AsyncMock()
+            mock_client.disconnect = AsyncMock()
+            mock_client.call_tool = AsyncMock(
+                return_value="Search results: Introduction to agents, Tool integration guide"
+            )
+            mock_client_class.return_value = mock_client
+
+            task = Task(
+                description=(
+                    "Search the knowledge base for information about CrewAI agents. "
+                    "No filters needed — just a general search."
+                ),
+                expected_output="A brief summary of relevant search results.",
+                agent=agent,
+            )
+
+            crew = Crew(agents=[agent], tasks=[task])
+            result = crew.kickoff()
+
+        assert result is not None
+        assert result.raw is not None
+
+    @pytest.mark.vcr()
+    def test_bedrock_mcp_tool_with_optional_fields_no_schema_error(self) -> None:
+        """MCP tools with Optional fields must not cause Bedrock schema validation errors.
+
+        Regression test for https://github.com/crewAIInc/crewAI/issues/4472.
+
+        Before the fix, generate_model_description() added additionalProperties:false
+        to the tool schema. The Bedrock Converse API rejects this field, raising:
+          "JSON schema is invalid"
+
+        With the fix, strip_openai_specific_schema_fields() removes it before the
+        schema is sent to Bedrock.
+
+        Note: MCPClient is mocked — no Bedrock credentials are needed to run this
+        test. The LLM call is replayed from the VCR cassette.
+
+        To record the cassette:
+            AWS_ACCESS_KEY_ID=<key> AWS_SECRET_ACCESS_KEY=<secret> AWS_DEFAULT_REGION=us-east-1 \\
+            uv run pytest tests/agents/test_native_tool_calling.py \\
+                ::TestNativeToolCallingBehavior::test_bedrock_mcp_tool_with_optional_fields_no_schema_error \\
+                --record-mode=once -v
+        """
+        from unittest.mock import AsyncMock, patch
+
+        from crewai.mcp.config import MCPServerStdio
+
+        stdio_config = MCPServerStdio(command="python", args=["fake_mcp_server.py"])
+
+        agent = Agent(
+            role="Knowledge Researcher",
+            goal="Search a knowledge base and return relevant results",
+            backstory="You are a helpful researcher who uses the filtered_search tool.",
+            mcps=[stdio_config],
+            llm=LLM(model="bedrock/anthropic.claude-3-haiku-20240307-v1:0"),
+            verbose=False,
+            max_iter=3,
+        )
+
+        with patch("crewai.agent.core.MCPClient") as mock_client_class:
+            mock_client = AsyncMock()
+            mock_client.list_tools = AsyncMock(
+                return_value=_MCP_OPTIONAL_FIELDS_TOOL_DEFS
+            )
+            mock_client.connected = False
+            mock_client.connect = AsyncMock()
+            mock_client.disconnect = AsyncMock()
+            mock_client.call_tool = AsyncMock(
+                return_value="Search results: Introduction to agents, Tool integration guide"
+            )
+            mock_client_class.return_value = mock_client
+
+            task = Task(
+                description=(
+                    "Search the knowledge base for information about CrewAI agents. "
+                    "No filters needed — just a general search."
+                ),
+                expected_output="A brief summary of relevant search results.",
+                agent=agent,
+            )
+
+            crew = Crew(agents=[agent], tasks=[task])
+            result = crew.kickoff()
+
+        assert result is not None
+        assert result.raw is not None
+
 
 # =============================================================================
 # Token Usage Tests

lib/crewai/tests/cassettes/agents/TestNativeToolCallingBehavior.test_bedrock_mcp_tool_with_optional_fields_no_schema_error.yaml

@@ -0,0 +1,128 @@
+interactions:
+- request:
+    body: '{"messages": [{"role": "user", "content": [{"text": "\nCurrent Task: Search
+      the knowledge base for information about CrewAI agents. No filters needed \u2014
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+      A brief summary of relevant search results.\nyou MUST return the actual complete
+      content as the final answer, not a summary."}]}], "inferenceConfig": {"stopSequences":
+      ["\nObservation:"]}, "system": [{"text": "You are Knowledge Researcher. You
+      are a helpful researcher who uses the filtered_search tool.\nYour personal goal
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+      [{"toolSpec": {"name": "python_fake_mcp_server_py_filtered_search", "description":
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+      filter", "title": "Filter Type"}, "page_id": {"anyOf": [{"type": "string"},
+      {"type": "object"}, {"type": "null"}], "default": null, "description": "Optional
+      page UUID", "title": "Page Id"}}, "required": ["query", "filter_type", "page_id"],
+      "type": "object"}}}}]}}'
+    headers:
+      Content-Length:
+      - '1324'
+      Content-Type:
+      - !!binary |
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+      User-Agent:
+      - X-USER-AGENT-XXX
+      amz-sdk-invocation-id:
+      - AMZ-SDK-INVOCATION-ID-XXX
+      amz-sdk-request:
+      - !!binary |
+        YXR0ZW1wdD0x
+      authorization:
+      - AUTHORIZATION-XXX
+      x-amz-date:
+      - X-AMZ-DATE-XXX
+    method: POST
+    uri: https://bedrock-runtime.us-east-1.amazonaws.com/model/anthropic.claude-3-haiku-20240307-v1%3A0/converse
+  response:
+    body:
+      string: '{"metrics":{"latencyMs":1866},"output":{"message":{"content":[{"text":"Understood.
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+    headers:
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+      x-amzn-RequestId:
+      - X-AMZN-REQUESTID-XXX
+    status:
+      code: 200
+      message: OK
+- request:
+    body: '{"messages": [{"role": "user", "content": [{"text": "\nCurrent Task: Search
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+      A brief summary of relevant search results.\nyou MUST return the actual complete
+      content as the final answer, not a summary."}]}, {"role": "assistant", "content":
+      [{"toolUse": {"toolUseId": "tooluse_DH3PzO6KYfG8Zn9tGJZrvL", "name": "python_fake_mcp_server_py_filtered_search",
+      "input": {}}}]}, {"role": "user", "content": [{"toolResult": {"toolUseId": "tooluse_DH3PzO6KYfG8Zn9tGJZrvL",
+      "content": [{"text": "Search results: Introduction to agents, Tool integration
+      guide"}]}}]}, {"role": "user", "content": [{"text": "Analyze the tool result.
+      If requirements are met, provide the Final Answer. Otherwise, call the next
+      tool. Deliver only the answer without meta-commentary."}]}], "inferenceConfig":
+      {"stopSequences": ["\nObservation:"]}, "system": [{"text": "You are Knowledge
+      Researcher. You are a helpful researcher who uses the filtered_search tool.\nYour
+      personal goal is: Search a knowledge base and return relevant results"}], "toolConfig":
+      {"tools": [{"toolSpec": {"name": "python_fake_mcp_server_py_filtered_search",
+      "description": "Search a knowledge base. Pass null for any filter you don''t
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+      query string", "title": "Query", "type": "string"}, "filter_type": {"anyOf":
+      [{"type": "string"}, {"type": "object"}, {"type": "null"}], "default": null,
+      "description": "Optional content-type filter", "title": "Filter Type"}, "page_id":
+      {"anyOf": [{"type": "string"}, {"type": "object"}, {"type": "null"}], "default":
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+      ["query", "filter_type", "page_id"], "type": "object"}}}}]}}'
+    headers:
+      Content-Length:
+      - '1873'
+      Content-Type:
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+      authorization:
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+    method: POST
+    uri: https://bedrock-runtime.us-east-1.amazonaws.com/model/anthropic.claude-3-haiku-20240307-v1%3A0/converse
+  response:
+    body:
+      string: '{"metrics":{"latencyMs":2324},"output":{"message":{"content":[{"text":"<result>\nIntroduction
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+        decision-making, and event-driven automation.\n\nTool integration guide:\nThe
+        CrewAI tool integration guide provides detailed instructions on how to integrate
+        CrewAI agents into your existing software systems and workflows. It covers
+        topics such as agent deployment, configuration, communication protocols, and
+        monitoring/reporting. The guide includes code samples and best practices to
+        ensure seamless integration.\n</result>"}],"role":"assistant"}},"stopReason":"end_turn","usage":{"inputTokens":680,"outputTokens":154,"serverToolUsage":{},"totalTokens":834}}'
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+      - keep-alive
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+      x-amzn-RequestId:
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+    status:
+      code: 200
+      message: OK
+version: 1

lib/crewai/tests/cassettes/agents/TestNativeToolCallingBehavior.test_gemini_mcp_tool_with_optional_fields_no_schema_error.yaml

@@ -0,0 +1,161 @@
+interactions:
+- request:
+    body: '{"contents": [{"parts": [{"text": "\nCurrent Task: Search the knowledge
+      base for information about CrewAI agents. No filters needed \u2014 just a general
+      search.\n\nThis is the expected criteria for your final answer: A brief summary
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+      "type": "object"}}]}], "generationConfig": {"stopSequences": ["\nObservation:"]}}'
+    headers:
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+      message: OK
+- request:
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+      base for information about CrewAI agents. No filters needed \u2014 just a general
+      search.\n\nThis is the expected criteria for your final answer: A brief summary
+      of relevant search results.\nyou MUST return the actual complete content as
+      the final answer, not a summary."}], "role": "user"}, {"parts": [{"functionCall":
+      {"args": {"query": "CrewAI agents"}, "name": "python_fake_mcp_server_py_filtered_search"}}],
+      "role": "model"}, {"parts": [{"functionResponse": {"name": "python_fake_mcp_server_py_filtered_search",
+      "response": {"result": "Search results: Introduction to agents, Tool integration
+      guide"}}}], "role": "user"}, {"parts": [{"text": "Analyze the tool result. If
+      requirements are met, provide the Final Answer. Otherwise, call the next tool.
+      Deliver only the answer without meta-commentary."}], "role": "user"}], "systemInstruction":
+      {"parts": [{"text": "You are Knowledge Researcher. You are a helpful researcher
+      who uses the filtered_search tool.\nYour personal goal is: Search a knowledge
+      base and return relevant results"}], "role": "user"}, "tools": [{"functionDeclarations":
+      [{"description": "Search a knowledge base. Pass null for any filter you don''t
+      need.", "name": "python_fake_mcp_server_py_filtered_search", "parameters_json_schema":
+      {"properties": {"query": {"description": "Search query string", "title": "Query",
+      "type": "string"}, "filter_type": {"anyOf": [{"type": "string"}, {"type": "object"},
+      {"type": "null"}], "default": null, "description": "Optional content-type filter",
+      "title": "Filter Type"}, "page_id": {"anyOf": [{"type": "string"}, {"type":
+      "object"}, {"type": "null"}], "default": null, "description": "Optional page
+      UUID", "title": "Page Id"}}, "required": ["query", "filter_type", "page_id"],
+      "type": "object"}}]}], "generationConfig": {"stopSequences": ["\nObservation:"]}}'
+    headers:
+      User-Agent:
+      - X-USER-AGENT-XXX
+      accept:
+      - '*/*'
+      accept-encoding:
+      - ACCEPT-ENCODING-XXX
+      connection:
+      - keep-alive
+      content-length:
+      - '1893'
+      content-type:
+      - application/json
+      host:
+      - generativelanguage.googleapis.com
+      x-goog-api-client:
+      - google-genai-sdk/1.49.0 gl-python/3.13.3
+      x-goog-api-key:
+      - X-GOOG-API-KEY-XXX
+    method: POST
+    uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent
+  response:
+    body:
+      string: "{\n  \"candidates\": [\n    {\n      \"content\": {\n        \"parts\":
+        [\n          {\n            \"text\": \"Search results: Introduction to agents,
+        Tool integration guide\"\n          }\n        ],\n        \"role\": \"model\"\n
+        \     },\n      \"finishReason\": \"STOP\",\n      \"index\": 0\n    }\n  ],\n
+        \ \"usageMetadata\": {\n    \"promptTokenCount\": 299,\n    \"candidatesTokenCount\":
+        10,\n    \"totalTokenCount\": 309,\n    \"promptTokensDetails\": [\n      {\n
+        \       \"modality\": \"TEXT\",\n        \"tokenCount\": 299\n      }\n    ]\n
+        \ },\n  \"modelVersion\": \"gemini-2.5-flash\",\n  \"responseId\": \"bYCfacPnLsD4-8YP6p64sQs\"\n}\n"
+    headers:
+      Alt-Svc:
+      - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000
+      Content-Type:
+      - application/json; charset=UTF-8
+      Date:
+      - Wed, 25 Feb 2026 23:06:22 GMT
+      Server:
+      - scaffolding on HTTPServer2
+      Server-Timing:
+      - gfet4t7; dur=380
+      Transfer-Encoding:
+      - chunked
+      Vary:
+      - Origin
+      - X-Origin
+      - Referer
+      X-Content-Type-Options:
+      - X-CONTENT-TYPE-XXX
+      X-Frame-Options:
+      - X-FRAME-OPTIONS-XXX
+      X-XSS-Protection:
+      - '0'
+    status:
+      code: 200
+      message: OK
+version: 1

lib/crewai/tests/utilities/test_agent_utils.py

@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 import asyncio
-from typing import Any
+from typing import Any, Literal, Optional
 from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
@@ -235,6 +235,178 @@ def _make_mock_i18n() -> MagicMock:
     }.get(key, "")
     return mock_i18n
 
+class MCPStyleInput(BaseModel):
+    """Input schema mimicking an MCP tool with optional fields."""
+
+    query: str = Field(description="Search query")
+    filter_type: Optional[Literal["internal", "user"]] = Field(
+        default=None, description="Filter type"
+    )
+    page_id: Optional[str] = Field(
+        default=None, description="Page UUID"
+    )
+
+
+class MCPStyleTool(BaseTool):
+    """A tool mimicking MCP tool schemas with optional fields."""
+
+    name: str = "mcp_search"
+    description: str = "Search with optional filters"
+    args_schema: type[BaseModel] = MCPStyleInput
+
+    def _run(self, **kwargs: Any) -> str:
+        return "result"
+
+
+class TestOptionalFieldsPreserveNull:
+    """Tests that optional tool fields preserve null in the schema."""
+
+    def test_optional_string_allows_null(self) -> None:
+        """Optional[str] fields should include null in the schema so the LLM
+        can send null instead of being forced to guess a value."""
+        tools = [MCPStyleTool()]
+        schemas, _ = convert_tools_to_openai_schema(tools)
+
+        params = schemas[0]["function"]["parameters"]
+        page_id_prop = params["properties"]["page_id"]
+
+        assert "anyOf" in page_id_prop
+        type_options = [opt.get("type") for opt in page_id_prop["anyOf"]]
+        assert "string" in type_options
+        assert "null" in type_options
+
+    def test_optional_literal_allows_null(self) -> None:
+        """Optional[Literal[...]] fields should include null."""
+        tools = [MCPStyleTool()]
+        schemas, _ = convert_tools_to_openai_schema(tools)
+
+        params = schemas[0]["function"]["parameters"]
+        filter_prop = params["properties"]["filter_type"]
+
+        assert "anyOf" in filter_prop
+        has_null = any(opt.get("type") == "null" for opt in filter_prop["anyOf"])
+        assert has_null
+
+    def test_required_field_stays_non_null(self) -> None:
+        """Required fields without Optional should NOT have null."""
+        tools = [MCPStyleTool()]
+        schemas, _ = convert_tools_to_openai_schema(tools)
+
+        params = schemas[0]["function"]["parameters"]
+        query_prop = params["properties"]["query"]
+
+        assert query_prop.get("type") == "string"
+        assert "anyOf" not in query_prop
+
+    def test_all_fields_in_required_for_strict_mode(self) -> None:
+        """All fields (including optional) must be in required for strict mode."""
+        tools = [MCPStyleTool()]
+        schemas, _ = convert_tools_to_openai_schema(tools)
+
+        params = schemas[0]["function"]["parameters"]
+        assert "query" in params["required"]
+        assert "filter_type" in params["required"]
+        assert "page_id" in params["required"]
+
+
+class TestStripOpenAISpecificSchemaFields:
+    """Tests for strip_openai_specific_schema_fields.
+
+    All tests call the real schema pipeline with real tool instances —
+    no mocking of internal functions.
+    """
+
+    def test_removes_additional_properties_at_root(self) -> None:
+        """additionalProperties must be absent after stripping."""
+        from crewai.utilities.pydantic_schema_utils import (
+            strip_openai_specific_schema_fields,
+        )
+
+        tools = [MCPStyleTool()]
+        schemas, _ = convert_tools_to_openai_schema(tools)
+        params = schemas[0]["function"]["parameters"]
+
+        # Confirm it's present in the OpenAI-format schema
+        assert params.get("additionalProperties") is False
+
+        clean = strip_openai_specific_schema_fields(params)
+        assert "additionalProperties" not in clean
+
+    def test_removes_additional_properties_recursively(self) -> None:
+        """Nested objects must also have additionalProperties removed."""
+        from crewai.utilities.pydantic_schema_utils import (
+            strip_openai_specific_schema_fields,
+        )
+
+        class NestedInput(BaseModel):
+            config: dict[str, Any] = Field(default_factory=dict)
+            query: str = Field(description="query")
+
+        class NestedTool(BaseTool):
+            name: str = "nested_tool"
+            description: str = "tool with nested object"
+            args_schema: type[BaseModel] = NestedInput
+
+            def _run(self, **kwargs: Any) -> str:
+                return "ok"
+
+        schemas, _ = convert_tools_to_openai_schema([NestedTool()])
+        params = schemas[0]["function"]["parameters"]
+        clean = strip_openai_specific_schema_fields(params)
+
+        def has_additional_properties(obj: Any) -> bool:
+            if isinstance(obj, dict):
+                if "additionalProperties" in obj:
+                    return True
+                return any(has_additional_properties(v) for v in obj.values())
+            if isinstance(obj, list):
+                return any(has_additional_properties(i) for i in obj)
+            return False
+
+        assert not has_additional_properties(clean)
+
+    def test_preserves_required_and_properties(self) -> None:
+        """required and properties must survive the strip."""
+        from crewai.utilities.pydantic_schema_utils import (
+            strip_openai_specific_schema_fields,
+        )
+
+        tools = [MCPStyleTool()]
+        schemas, _ = convert_tools_to_openai_schema(tools)
+        params = schemas[0]["function"]["parameters"]
+        clean = strip_openai_specific_schema_fields(params)
+
+        assert "properties" in clean
+        assert "required" in clean
+        assert "query" in clean["properties"]
+
+    def test_gemini_bedrock_path_extracts_clean_parameters(self) -> None:
+        """extract_tool_info returns the same parameters dict that
+        strip_openai_specific_schema_fields will clean — verifying the
+        full Gemini/Bedrock conversion pipeline produces a valid schema."""
+        from crewai.llms.providers.utils.common import extract_tool_info
+        from crewai.utilities.pydantic_schema_utils import (
+            strip_openai_specific_schema_fields,
+        )
+
+        tools = [MCPStyleTool()]
+        schemas, _ = convert_tools_to_openai_schema(tools)
+
+        # extract_tool_info is what Gemini/Bedrock call internally
+        _name, _desc, parameters = extract_tool_info(schemas[0])
+
+        assert parameters.get("additionalProperties") is False, (
+            "Raw OpenAI schema should have additionalProperties=false"
+        )
+
+        clean = strip_openai_specific_schema_fields(parameters)
+        assert "additionalProperties" not in clean
+        # null types preserved for optional fields
+        assert any(
+            opt.get("type") == "null"
+            for opt in clean["properties"]["page_id"].get("anyOf", [])
+        )
+
 
 class TestSummarizeMessages:
     """Tests for summarize_messages function."""