Merge pull request #25396 from BerriAI/litellm_bedrock-normalize-custom-tool-schema

feat(bedrock): normalize custom tool JSON schema for Invoke and Converse

litellm/litellm_core_utils/prompt_templates/factory.py

@@ -5144,26 +5144,44 @@ def _bedrock_tools_pt(tools: List) -> List[BedrockToolBlock]:
         }
     ]
     """
+    from litellm.llms.bedrock.common_utils import (
+        normalize_json_schema_custom_types_to_object,
+    )
     from litellm.litellm_core_utils.prompt_templates.common_utils import unpack_defs
 
+    _valid_json_schema_root_types = frozenset(
+        ("array", "boolean", "integer", "null", "number", "object", "string")
+    )
     tool_block_list: List[BedrockToolBlock] = []
-    for tool in tools:
+    for tool_idx, tool in enumerate(tools):
         # Check if tool is already a BedrockToolBlock (e.g., systemTool for Nova grounding)
         if _is_bedrock_tool_block(tool):
             # Already a BedrockToolBlock, pass it through
             tool_block_list.append(tool)  # type: ignore
             continue
 
-        # Handle regular OpenAI-style function tools
-        parameters = tool.get("function", {}).get(
-            "parameters", {"type": "object", "properties": {}}
-        )
-        name = tool.get("function", {}).get("name", "")
+        # OpenAI function tools, or Anthropic Messages / Claude Code ({name, input_schema, type, ...})
+        if isinstance(tool, dict) and "input_schema" in tool and "function" not in tool:
+            parameters = copy.deepcopy(
+                tool.get("input_schema") or {"type": "object", "properties": {}}
+            )
+            raw_name = tool.get("name", "") or ""
+            _tool_description = tool.get("description", None)
+        else:
+            parameters = copy.deepcopy(
+                tool.get("function", {}).get(
+                    "parameters", {"type": "object", "properties": {}}
+                )
+            )
+            raw_name = tool.get("function", {}).get("name", "") or ""
+            _tool_description = tool.get("function", {}).get("description", None)
+
+        if not (raw_name and str(raw_name).strip()):
+            raw_name = f"litellm_unnamed_tool_{tool_idx}"
 
         # related issue: https://github.com/BerriAI/litellm/issues/5007
         # Bedrock tool names must satisfy regular expression pattern: [a-zA-Z][a-zA-Z0-9_]* ensure this is true
-        name = make_valid_bedrock_tool_name(input_tool_name=name)
-        _tool_description = tool.get("function", {}).get("description", None)
+        name = make_valid_bedrock_tool_name(input_tool_name=raw_name)
         if _tool_description:  # bedrock doesn't accept empty "" or None descriptions
             description = _tool_description
         else:
@@ -5176,9 +5194,12 @@ def _bedrock_tools_pt(tools: List) -> List[BedrockToolBlock]:
         # with circular references (see issue #19098). unpack_defs handles nested
         # refs recursively and correctly detects/skips circular references.
         unpack_defs(parameters, defs_copy)
+        normalize_json_schema_custom_types_to_object(parameters)
+        if parameters.get("type") not in _valid_json_schema_root_types:
+            parameters["type"] = "object"
         tool_input_schema = BedrockToolInputSchemaBlock(
             json=BedrockToolJsonSchemaBlock(
-                type=parameters.get("type", ""),
+                type=parameters["type"],
                 properties=parameters.get("properties", {}),
                 required=parameters.get("required", []),
             )

litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py

@@ -796,7 +796,7 @@ class LiteLLMAnthropicMessagesAdapter:
         tool_name_mapping: Dict[str, str] = {}
         mapped_tool_params = ["name", "input_schema", "description", "cache_control"]
 
-        for tool in tools:
+        for idx, tool in enumerate(tools):
             # Check if this is an Anthropic-native tool that should be kept as-is
             tool_type = tool.get("type", "")
             if any(tool_type.startswith(t.value) for t in ANTHROPIC_HOSTED_TOOLS):
@@ -804,7 +804,13 @@ class LiteLLMAnthropicMessagesAdapter:
                 new_tools.append(tool)  # type: ignore[arg-type]
                 continue
 
-            original_name = tool["name"]
+            raw_name = tool.get("name")
+            if raw_name is None or (
+                isinstance(raw_name, str) and not str(raw_name).strip()
+            ):
+                original_name = f"litellm_unnamed_tool_{idx}"
+            else:
+                original_name = str(raw_name)
             truncated_name = truncate_tool_name(original_name)
 
             # Store mapping if name was truncated

litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py

@@ -16,6 +16,7 @@ from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation
 )
 from litellm.llms.bedrock.common_utils import (
     get_anthropic_beta_from_headers,
+    normalize_tool_input_schema_types_for_bedrock_invoke,
     remove_custom_field_from_tools,
 )
 from litellm.types.llms.anthropic import ANTHROPIC_TOOL_SEARCH_BETA_HEADER
@@ -174,6 +175,7 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig):
 
         # Remove `custom` field from tools (Bedrock doesn't support it)
         remove_custom_field_from_tools(anthropic_request)
+        normalize_tool_input_schema_types_for_bedrock_invoke(anthropic_request)
         return anthropic_request
 
     def _compute_bedrock_invoke_beta_headers(

litellm/llms/bedrock/common_utils.py

@@ -6,7 +6,7 @@ Common utilities used across bedrock chat/embedding/image generation
 
 import json
 import os
-from typing import TYPE_CHECKING, Dict, List, Literal, Optional, Union
+from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Union
 
 if TYPE_CHECKING:
     from litellm.types.llms.bedrock import BedrockCreateBatchRequest
@@ -70,6 +70,88 @@ def remove_custom_field_from_tools(request_body: dict) -> None:
             tool.pop("custom", None)
 
 
+def normalize_json_schema_custom_types_to_object(schema: dict) -> None:
+    """
+    In-place: replace JSON Schema ``type: \"custom\"`` with ``\"object\"`` (iterative walk).
+
+    Anthropic / Claude Code use ``custom`` for tool schemas; Bedrock Invoke and
+    Bedrock Converse only accept standard JSON Schema type strings.
+
+    Uses an explicit stack (not recursion) to satisfy recursive-function guards in CI.
+    """
+    stack: List[Any] = [schema]
+    seen: set[int] = set()
+    while stack:
+        node = stack.pop()
+        if not isinstance(node, dict):
+            continue
+        node_id = id(node)
+        if node_id in seen:
+            continue
+        seen.add(node_id)
+        if node.get("type") == "custom":
+            node["type"] = "object"
+        items = node.get("items")
+        if isinstance(items, dict):
+            stack.append(items)
+        addl = node.get("additionalProperties")
+        if isinstance(addl, dict):
+            stack.append(addl)
+        props = node.get("properties")
+        if isinstance(props, dict):
+            for sub in props.values():
+                if isinstance(sub, dict):
+                    stack.append(sub)
+        for combiner in ("allOf", "anyOf", "oneOf"):
+            arr = node.get(combiner)
+            if isinstance(arr, list):
+                for sub in arr:
+                    if isinstance(sub, dict):
+                        stack.append(sub)
+
+
+def normalize_tool_input_schema_types_for_bedrock_invoke(request_body: dict) -> None:
+    """
+    Bedrock Invoke (Anthropic Messages) validates ``input_schema`` as JSON Schema.
+    Anthropic's API allows ``type: \"custom\"`` for Claude Code custom tools; Bedrock
+    rejects it with: ``tools.0.custom.input_schema.type: Input should be 'object'``.
+
+    Normalizes ``type: \"custom\"`` to ``\"object\"`` throughout each tool's
+    ``input_schema`` (recursive for nested properties, items, combinators).
+
+    Args:
+        request_body: Request dictionary to modify in-place.
+    """
+    tools = request_body.get("tools")
+    if not tools or not isinstance(tools, list):
+        return
+    for tool in tools:
+        if not isinstance(tool, dict):
+            continue
+        input_schema = tool.get("input_schema")
+        if isinstance(input_schema, dict):
+            normalize_json_schema_custom_types_to_object(input_schema)
+
+
+def ensure_bedrock_anthropic_messages_tool_names(request_body: dict) -> None:
+    """
+    Bedrock Invoke (Anthropic Messages) requires each tool to include ``name``.
+    Some clients send only ``input_schema``; Bedrock then errors with
+    ``tools.0.custom.name: Field required``.
+
+    In-place: set ``name`` to ``litellm_unnamed_tool_{index}`` when missing or blank.
+    """
+    tools = request_body.get("tools")
+    if not tools or not isinstance(tools, list):
+        return
+    for i, tool in enumerate(tools):
+        if not isinstance(tool, dict):
+            continue
+        name = tool.get("name")
+        if name is None or (isinstance(name, str) and not name.strip()):
+            tool["name"] = f"litellm_unnamed_tool_{i}"
+
+
 class AmazonBedrockGlobalConfig:
     def __init__(self):
         pass

litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py

@@ -25,8 +25,10 @@ from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation
     AmazonInvokeConfig,
 )
 from litellm.llms.bedrock.common_utils import (
+    ensure_bedrock_anthropic_messages_tool_names,
     get_anthropic_beta_from_headers,
     is_claude_4_5_on_bedrock,
+    normalize_tool_input_schema_types_for_bedrock_invoke,
     remove_custom_field_from_tools,
 )
 from litellm.types.llms.anthropic import ANTHROPIC_TOOL_SEARCH_BETA_HEADER
@@ -426,6 +428,8 @@ class AmazonAnthropicClaudeMessagesConfig(
         # which causes Bedrock to reject the request with "Extra inputs are not permitted"
         # Ref: https://github.com/BerriAI/litellm/issues/22847
         remove_custom_field_from_tools(anthropic_messages_request)
+        normalize_tool_input_schema_types_for_bedrock_invoke(anthropic_messages_request)
+        ensure_bedrock_anthropic_messages_tool_names(anthropic_messages_request)
 
         # 6. AUTO-INJECT beta headers based on features used
         anthropic_model_info = AnthropicModelInfo()

tests/llm_translation/test_bedrock_completion.py

@@ -1066,6 +1066,72 @@ def test_bedrock_tools_pt_invalid_names():
     assert result[1]["toolSpec"]["name"] == "another_invalid_name"
 
 
+def test_bedrock_converse_tools_pt_converts_custom_schema_type_to_object():
+    """
+    Bedrock Converse ``toolSpec.inputSchema.json`` must use standard JSON Schema
+    types. Anthropic / Claude Code use ``type: \"custom\"`` in ``input_schema`` (or
+    OpenAI ``parameters``); ``_bedrock_tools_pt`` must convert ``custom`` → ``object``
+    at the root and inside nested ``properties``.
+    """
+    tools = [
+        {
+            "name": "Agent",
+            "description": "Subagent tool",
+            "type": "custom",
+            "input_schema": {
+                "type": "custom",
+                "additionalProperties": False,
+                "properties": {
+                    "prompt": {"type": "string"},
+                    "nested": {
+                        "type": "custom",
+                        "properties": {"x": {"type": "string"}},
+                        "required": ["x"],
+                    },
+                },
+                "required": ["prompt"],
+            },
+        },
+        {
+            "type": "function",
+            "function": {
+                "name": "other",
+                "description": "x",
+                "parameters": {
+                    "type": "custom",
+                    "properties": {
+                        "a": {"type": "integer"},
+                        "nested_obj": {
+                            "type": "custom",
+                            "properties": {"b": {"type": "string"}},
+                        },
+                    },
+                    "required": ["a"],
+                },
+            },
+        },
+        {
+            "input_schema": {
+                "type": "object",
+                "properties": {"q": {"type": "string"}},
+            },
+        },
+    ]
+
+    result = _bedrock_tools_pt(tools)
+
+    assert result[0]["toolSpec"]["name"] == "Agent"
+    j0 = result[0]["toolSpec"]["inputSchema"]["json"]
+    assert j0["type"] == "object"
+    assert j0["properties"]["nested"]["type"] == "object"
+
+    j1 = result[1]["toolSpec"]["inputSchema"]["json"]
+    assert j1["type"] == "object"
+    assert j1["properties"]["nested_obj"]["type"] == "object"
+
+    assert result[2]["toolSpec"]["name"] == "litellm_unnamed_tool_2"
+
+
 def test_bedrock_tools_transformation_valid_params():
     from litellm.types.llms.bedrock import ToolJsonSchemaBlock
 

tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py

@@ -1420,6 +1420,24 @@ def test_cache_control_not_preserved_in_tools_for_non_claude():
     assert "cache_control" not in result[0]
 
 
+def test_translate_anthropic_tools_to_openai_fills_missing_tool_name():
+    """Schema-only tools (no ``name``) must not crash the Converse adapter path."""
+    tools = [
+        {
+            "input_schema": {
+                "type": "object",
+                "properties": {"q": {"type": "string"}},
+                "required": ["q"],
+            },
+        },
+        {"name": "", "input_schema": {"type": "object", "properties": {}}},
+    ]
+    adapter = LiteLLMAnthropicMessagesAdapter()
+    result, _ = adapter.translate_anthropic_tools_to_openai(tools=tools, model=None)
+    assert result[0]["function"]["name"] == "litellm_unnamed_tool_0"
+    assert result[1]["function"]["name"] == "litellm_unnamed_tool_1"
+
+
 def test_translate_openai_content_to_anthropic_reasoning_content_without_thinking_blocks():
     """
     Test that reasoning_content is converted to thinking block when thinking_blocks is not present.

tests/test_litellm/llms/bedrock/messages/invoke_transformations/test_anthropic_claude3_transformation.py

@@ -1,4 +1,5 @@
 import asyncio
+import copy
 import json
 import os
 import sys
@@ -12,7 +13,11 @@ import pytest
 sys.path.insert(0, os.path.abspath("../../../../../.."))
 
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
-from litellm.llms.bedrock.common_utils import remove_custom_field_from_tools
+from litellm.llms.bedrock.common_utils import (
+    ensure_bedrock_anthropic_messages_tool_names,
+    normalize_tool_input_schema_types_for_bedrock_invoke,
+    remove_custom_field_from_tools,
+)
 from litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation import (
     AmazonAnthropicClaudeMessagesConfig,
     AmazonAnthropicClaudeMessagesStreamDecoder,
@@ -294,6 +299,143 @@ def test_remove_custom_field_from_tools():
     assert request4["tools"] is None
 
 
+def test_normalize_tool_input_schema_types_for_bedrock_invoke():
+    """
+    Claude Code sends ``input_schema.type: \"custom\"`` for custom tools.
+    Bedrock Invoke rejects this; it requires JSON Schema ``type: \"object\"``.
+    """
+
+    request = {
+        "tools": [
+            {
+                "name": "Agent",
+                "type": "custom",
+                "description": "subagent",
+                "input_schema": {
+                    "type": "custom",
+                    "additionalProperties": False,
+                    "properties": {
+                        "nested": {"type": "custom", "properties": {"x": {"type": "string"}}}
+                    },
+                    "required": ["nested"],
+                },
+            },
+            {
+                "name": "Read",
+                "input_schema": {"type": "object", "properties": {}},
+            },
+        ]
+    }
+
+    normalize_tool_input_schema_types_for_bedrock_invoke(request)
+
+    agent_tool = request["tools"][0]
+    assert agent_tool["type"] == "custom"
+    assert agent_tool["input_schema"]["type"] == "object"
+    assert agent_tool["input_schema"]["properties"]["nested"]["type"] == "object"
+    assert request["tools"][1]["input_schema"]["type"] == "object"
+
+    request2 = {"messages": []}
+    normalize_tool_input_schema_types_for_bedrock_invoke(request2)
+    assert request2 == {"messages": []}
+
+
+def test_ensure_bedrock_anthropic_messages_tool_names():
+    request = {
+        "tools": [
+            {"input_schema": {"type": "object", "properties": {}}},
+            {"name": "", "input_schema": {"type": "object", "properties": {}}},
+            {"name": "  ", "input_schema": {"type": "object", "properties": {}}},
+            {"name": "KeepMe", "input_schema": {"type": "object", "properties": {}}},
+        ]
+    }
+    ensure_bedrock_anthropic_messages_tool_names(request)
+    assert request["tools"][0]["name"] == "litellm_unnamed_tool_0"
+    assert request["tools"][1]["name"] == "litellm_unnamed_tool_1"
+    assert request["tools"][2]["name"] == "litellm_unnamed_tool_2"
+    assert request["tools"][3]["name"] == "KeepMe"
+
+
+def test_bedrock_invoke_messages_transform_adds_name_when_tool_missing_name():
+    """Bedrock requires tools.0.custom.name when the payload is schema-only."""
+    from litellm.types.router import GenericLiteLLMParams
+
+    cfg = AmazonAnthropicClaudeMessagesConfig()
+    optional_params = {
+        "max_tokens": 128,
+        "tools": [
+            {
+                "input_schema": {
+                    "type": "object",
+                    "properties": {"questions": {"type": "array"}},
+                    "required": ["questions"],
+                },
+            }
+        ],
+        "stream": False,
+    }
+    result = cfg.transform_anthropic_messages_request(
+        model="anthropic.claude-3-haiku-20240307-v1:0",
+        messages=[{"role": "user", "content": "hi"}],
+        anthropic_messages_optional_request_params=copy.deepcopy(optional_params),
+        litellm_params=GenericLiteLLMParams(),
+        headers={},
+    )
+    assert result["tools"][0]["name"] == "litellm_unnamed_tool_0"
+
+
+def test_bedrock_invoke_messages_transform_converts_custom_tool_schema_type_to_object():
+    """
+    End-to-end: AmazonAnthropicClaudeMessagesConfig must emit Bedrock Invoke bodies
+    where every ``input_schema`` uses JSON Schema types (``object``), not Anthropic
+    ``type: \"custom\"`` (root and nested).
+    """
+    from litellm.types.router import GenericLiteLLMParams
+
+    cfg = AmazonAnthropicClaudeMessagesConfig()
+    tools = [
+        {
+            "name": "Agent",
+            "type": "custom",
+            "description": "Subagent",
+            "input_schema": {
+                "type": "custom",
+                "additionalProperties": False,
+                "properties": {
+                    "prompt": {"type": "string"},
+                    "nested": {
+                        "type": "custom",
+                        "properties": {"x": {"type": "string"}},
+                        "required": ["x"],
+                    },
+                },
+                "required": ["prompt"],
+            },
+        }
+    ]
+    optional_params = {
+        "max_tokens": 256,
+        "tools": copy.deepcopy(tools),
+        "stream": False,
+    }
+    messages = [{"role": "user", "content": "hi"}]
+
+    result = cfg.transform_anthropic_messages_request(
+        model="anthropic.claude-3-haiku-20240307-v1:0",
+        messages=messages,
+        anthropic_messages_optional_request_params=optional_params,
+        litellm_params=GenericLiteLLMParams(),
+        headers={},
+    )
+
+    assert "tools" in result
+    schema = result["tools"][0]["input_schema"]
+    assert schema["type"] == "object"
+    assert schema["properties"]["nested"]["type"] == "object"
+    # Tool discriminator stays Anthropic-side; only input_schema is normalized
+    assert result["tools"][0]["type"] == "custom"
+
+
 def test_remove_scope_from_cache_control():
     """Ensure scope field is removed from cache_control for Bedrock (not supported)."""