arcade-mcp/libs/arcade-core/arcade_core/output.py
Eric Gustin f4558ef3a8
Tool Error Handling (#539)
# Improvements to Arcade TDK Error Handling
I tried my very best to not make any breaking changes in this PR. So,
you will notice various "Deprecation" notices throughout.

### Instructions for PR reviewers
1. Pull down this PR's branch
2. Pull down the Engine's tool error handling PR's branch
3. Update your installed arcadepy to have the following:
- In `arcadepy/resources/tools/tools.py`, if you want to test out
including stacktraces, then you need to update `ToolsResource.execute`
to accept a `include_error_stacktrace` argument and also include the
"include_error_stacktrace" argument to the POST to the Engine inside of
the function's execute method's body.
- In `arcadepy/types/execute_tool_response.py` add the following enum
      ```py
      class ErrorKind(str, Enum):
          """Error kind that is comprised of
          - the who (toolkit, tool, upstream)
          - the when (load time, definition parsing time, runtime)
- the what (bad_definition, bad_input, bad_output, retry,
context_required, fatal, etc.)"""
      
          TOOLKIT_LOAD_FAILED = "TOOLKIT_LOAD_FAILED"
TOOL_DEFINITION_BAD_DEFINITION = "TOOL_DEFINITION_BAD_DEFINITION"
TOOL_DEFINITION_BAD_INPUT_SCHEMA = "TOOL_DEFINITION_BAD_INPUT_SCHEMA"
TOOL_DEFINITION_BAD_OUTPUT_SCHEMA = "TOOL_DEFINITION_BAD_OUTPUT_SCHEMA"
          TOOL_RUNTIME_BAD_INPUT_VALUE = "TOOL_RUNTIME_BAD_INPUT_VALUE"
TOOL_RUNTIME_BAD_OUTPUT_VALUE = "TOOL_RUNTIME_BAD_OUTPUT_VALUE"
          TOOL_RUNTIME_RETRY = "TOOL_RUNTIME_RETRY"
TOOL_RUNTIME_CONTEXT_REQUIRED = "TOOL_RUNTIME_CONTEXT_REQUIRED"
          TOOL_RUNTIME_FATAL = "TOOL_RUNTIME_FATAL"
          UPSTREAM_RUNTIME_BAD_REQUEST = "UPSTREAM_RUNTIME_BAD_REQUEST"
          UPSTREAM_RUNTIME_AUTH_ERROR = "UPSTREAM_RUNTIME_AUTH_ERROR"
          UPSTREAM_RUNTIME_NOT_FOUND = "UPSTREAM_RUNTIME_NOT_FOUND"
UPSTREAM_RUNTIME_VALIDATION_ERROR = "UPSTREAM_RUNTIME_VALIDATION_ERROR"
          UPSTREAM_RUNTIME_RATE_LIMIT = "UPSTREAM_RUNTIME_RATE_LIMIT"
UPSTREAM_RUNTIME_SERVER_ERROR = "UPSTREAM_RUNTIME_SERVER_ERROR"
          UPSTREAM_RUNTIME_UNMAPPED = "UPSTREAM_RUNTIME_UNMAPPED"
          UNKNOWN = "UNKNOWN"
      ```
- In `arcadepy/types/execute_tool_response.py` add the following fields
to OutputError:
      ```py
      kind: ErrorKind
      status_code: Optional[int] = None
      stacktrace: Optional[str] = None
      extra: Optional[dict[str, Any]] = None
      ```
### Example Client Usage
```py
# Example of handling an upstream rate limit
error = response.output.error
if  error and error.kind == ErrorKind.UPSTREAM_RUNTIME_RATE_LIMIT:
    sleep_time = error.retry_after_ms / 1000
    time.sleep(sleep_time)
    # and then execute again
```
```py
# Examples of determining what type of runtime error it is
error = response.output.error
if error:
    is_retryable_error = error.kind == ErrorKind.TOOL_RUNTIME_RETRY
    is_a_bug_in_the_tool = error.kind == ErrorKind.TOOL_RUNTIME_FATAL
    is_additional_context_required = error.kind == ErrorKind.TOOL_RUNTIME_CONTEXT_REQUIRED
```

### Example Tool Usage
```py
# EXAMPLE 1 letting Arcade handle upstream error handling for you
reddit_client.post(params) # Arcade's httpx adapter will handle error handling for you!

# ------------------------------------

# EXAMPLE 2 handling upstream bad request yourself, but letting Arcade handle the rest
try:
    reddit_client.post(params)
except httpx.HTTPStatusError as e:
    if e.status_code == 400:
        raise UpstreamError("My extra custom message) from e
    raise
```
```py
# EXAMPLE 1 letting Arcade handle it for you
risky_element = my_risky_list[42] # Arcade will raise a FatalToolError for you

# ------------------------------------

# EXAMPLE 2 handling it yourself for extra flexibility
try:
    risky_element = my_risky_list[42]
except IndexError as e:
    raise FatalToolError("My extra custom message") from e
```
### Non-runtime Error Message Examples
Example ToolkitLoadError Messages:
```
- [TOOLKIT_LOAD_FAILED] ToolkitLoadError when loading toolkit 'sample_tool': Could not import module mock_module. Reason: Mock import error
- [TOOLKIT_LOAD_FAILED] ToolkitLoadError when loading toolkit 'test_toolkit': Tool 'ValidTool' in toolkit 'test_toolkit' already exists in the catalog.
```
Example ToolDefinitionError Messages
```
- [TOOL_DEFINITION_BAD_DEFINITION] ToolDefinitionError in definition of tool 'tool_missing_description': Tool 'tool_missing_description' is missing a description
- [TOOL_DEFINITION_BAD_DEFINITION] ToolDefinitionError in definition of tool 'tool_with_invalid_secret_type': Secret keys must be strings (error in tool ToolWithInvalidSecretType).
- [TOOL_DEFINITION_BAD_DEFINITION] ToolDefinitionError in definition of tool 'tool_with_empty_secret': Secrets must have a non-empty key (error in tool ToolWithEmptySecret).
- [TOOL_DEFINITION_BAD_DEFINITION] ToolDefinitionError in definition of tool 'tool_with_invalid_metadata_type': Metadata must be strings (error in tool ToolWithInvalidMetadataType).
- [TOOL_DEFINITION_BAD_DEFINITION] ToolDefinitionError in definition of tool 'tool_with_metadata_requiring_auth_without_auth': Tool ToolWithMetadataRequiringAuthWithoutAuth declares metadata key 'client_id', which requires that the tool has an auth requirement, but no auth requirement was provided. Please specify an auth requirement.
- [TOOL_DEFINITION_BAD_DEFINITION] ToolDefinitionError in definition of tool 'tool_with_empty_metadata': Metadata must have a non-empty key (error in tool ToolWithEmptyMetadata).
- [TOOL_DEFINITION_BAD_DEFINITION] ToolDefinitionError in definition of tool 'tool_with_unsupported_param_type': Unsupported parameter type: <class 'test_catalog.MyFancyTestClass'>
```
Example ToolInputSchemaError Messages
```
- [TOOL_DEFINITION_BAD_INPUT_SCHEMA] ToolInputSchemaError in definition of tool 'tool_with_missing_input_parameter_annotation': Parameter 'input_text' is missing a description
- [TOOL_DEFINITION_BAD_INPUT_SCHEMA] ToolInputSchemaError in definition of tool 'tool_with_no_type_annotation': Parameter param has no type annotation.
- [TOOL_DEFINITION_BAD_INPUT_SCHEMA] ToolInputSchemaError in definition of tool 'tool_with_invalid_param_name': Invalid parameter name: '123invalid' is not a valid identifier. Identifiers must start with a letter or underscore, and can only contain letters, digits, or underscores.
- [TOOL_DEFINITION_BAD_INPUT_SCHEMA] ToolInputSchemaError in definition of tool 'tool_with_too_many_annotations': Parameter param: Annotated[str, 'name', 'desc', 'extra'] has too many string annotations. Expected 0, 1, or 2, got 3.
- [TOOL_DEFINITION_BAD_INPUT_SCHEMA] ToolInputSchemaError in definition of tool 'tool_with_required_union_param': Parameter param is a union type. Only optional types are supported.
- [TOOL_DEFINITION_BAD_INPUT_SCHEMA] ToolInputSchemaError in definition of tool 'tool_with_non_callable_default_factory': Default factory for parameter param: Annotated[str, 'Parameter'] = FieldInfo(annotation=NoneType, required=False, default_factory=str) is not callable.
- [TOOL_DEFINITION_BAD_INPUT_SCHEMA] ToolInputSchemaError in definition of tool 'tool_with_multiple_tool_contexts': Only one ToolContext parameter is supported, but tool tool_with_multiple_tool_contexts has multiple.
```
Example ToolOutputSchemaError Messages
```
- [TOOL_DEFINITION_BAD_OUTPUT_SCHEMA] ToolOutputSchemaError in definition of tool 'tool_missing_return_type_hint': Tool 'ToolMissingReturnTypeHint' must have a return type
- [TOOL_DEFINITION_BAD_OUTPUT_SCHEMA] ToolOutputSchemaError in definition of tool 'tool_with_unsupported_output_type': Unsupported output type '<class 'test_catalog.MyFancyTestClass'>'. Only built-in Python types, TypedDicts, Pydantic models, and standard collections are supported as tool output types.
```
### Runtime Error Message Examples
Example Tool Runtime Error Messages
```
- [TOOL_RUNTIME_FATAL] FatalToolError during execution of tool 'get_posts_in_subreddit': list index out of range
- [TOOL_RUNTIME_CONTEXT_REQUIRED] ContextRequiredToolError during execution of tool 'get_posts_in_subreddit': Ambiguous username. Please provide a more specific username
- [TOOL_RUNTIME_RETRY] RetryableToolError during execution of tool 'get_posts_in_subreddit': Retry with subreddit=learnpython or subreddit=learnprogramming
```

Example Upstream Runtime Error Messages
```
- [UPSTREAM_RUNTIME_RATE_LIMIT] UpstreamRateLimitError during execution of tool 'get_posts_in_subreddit': 429 Client Error: Too Many Requests
- [UPSTREAM_RUNTIME_BAD_REQUEST] UpstreamError during execution of tool 'get_posts_in_subreddit': 400 Client Error: Bad request. Missing 'id' parameter.
- [UPSTREAM_RUNTIME_BAD_REQUEST] UpstreamError during execution of tool 'search_files': Upstream Google API error: Invalid value '-23'. Values must be within the range: [value: 1\n, value: 1000\n]
```
2025-09-10 10:45:18 -07:00

123 lines
3.9 KiB
Python

from typing import Any, TypeVar
from pydantic import BaseModel
from arcade_core.errors import ErrorKind
from arcade_core.schema import ToolCallError, ToolCallLog, ToolCallOutput
from arcade_core.utils import coerce_empty_list_to_none
T = TypeVar("T")
class ToolOutputFactory:
"""
Singleton pattern for unified return method from tools.
"""
def success(
self,
*,
data: T | None = None,
logs: list[ToolCallLog] | None = None,
) -> ToolCallOutput:
# Extract the result value
"""
Extracts the result value for the tool output.
The executor guarantees that `data` is either a string, a dict, or None.
"""
value: str | int | float | bool | dict | list | None
if data is None:
value = ""
elif hasattr(data, "result"):
result = getattr(data, "result", "")
# Handle None result the same way as None data
if result is None:
value = ""
# If the result is a BaseModel (e.g., from TypedDict conversion), convert to dict
elif isinstance(result, BaseModel):
value = result.model_dump()
# If the result is a list, check if it contains BaseModel objects
elif isinstance(result, list):
value = [
item.model_dump() if isinstance(item, BaseModel) else item for item in result
]
else:
value = result
elif isinstance(data, BaseModel):
value = data.model_dump()
elif isinstance(data, (str, int, float, bool, list, dict)):
value = data
else:
raise ValueError(f"Unsupported data output type: {type(data)}")
logs = coerce_empty_list_to_none(logs)
return ToolCallOutput(
value=value,
logs=logs,
)
def fail(
self,
*,
message: str,
developer_message: str | None = None,
stacktrace: str | None = None,
logs: list[ToolCallLog] | None = None,
additional_prompt_content: str | None = None,
retry_after_ms: int | None = None,
kind: ErrorKind = ErrorKind.UNKNOWN,
can_retry: bool = False,
status_code: int | None = None,
extra: dict[str, Any] | None = None,
) -> ToolCallOutput:
return ToolCallOutput(
error=ToolCallError(
message=message,
developer_message=developer_message,
can_retry=can_retry,
additional_prompt_content=additional_prompt_content,
retry_after_ms=retry_after_ms,
stacktrace=stacktrace,
kind=kind,
status_code=status_code,
extra=extra,
),
logs=coerce_empty_list_to_none(logs),
)
def fail_retry(
self,
*,
message: str,
developer_message: str | None = None,
additional_prompt_content: str | None = None,
retry_after_ms: int | None = None,
stacktrace: str | None = None,
logs: list[ToolCallLog] | None = None,
kind: ErrorKind = ErrorKind.TOOL_RUNTIME_RETRY,
status_code: int = 500,
extra: dict[str, Any] | None = None,
) -> ToolCallOutput:
"""
DEPRECATED: Use ToolOutputFactory.fail instead.
This method will be removed in version 3.0.0
"""
return ToolCallOutput(
error=ToolCallError(
message=message,
developer_message=developer_message,
can_retry=True,
additional_prompt_content=additional_prompt_content,
retry_after_ms=retry_after_ms,
stacktrace=stacktrace,
kind=kind,
status_code=status_code,
extra=extra,
),
logs=coerce_empty_list_to_none(logs),
)
output_factory = ToolOutputFactory()