Update 2026-05-13 16:43:53
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yi
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"""Base class for agent tools."""
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from abc import ABC, abstractmethod
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from typing import Any
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class Tool(ABC):
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"""
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Abstract base class for agent tools.
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Tools are capabilities that the agent can use to interact with
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the environment, such as reading files, executing commands, etc.
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"""
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_TYPE_MAP = {
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"string": str,
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"integer": int,
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"number": (int, float),
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"boolean": bool,
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"array": list,
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"object": dict,
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}
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@staticmethod
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def _resolve_type(t: Any) -> str | None:
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"""Resolve JSON Schema type to a simple string.
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JSON Schema allows ``"type": ["string", "null"]`` (union types).
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We extract the first non-null type so validation/casting works.
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"""
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if isinstance(t, list):
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for item in t:
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if item != "null":
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return item
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return None
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return t
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@property
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@abstractmethod
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def name(self) -> str:
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"""Tool name used in function calls."""
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pass
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@property
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@abstractmethod
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def description(self) -> str:
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"""Description of what the tool does."""
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pass
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@property
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@abstractmethod
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def parameters(self) -> dict[str, Any]:
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"""JSON Schema for tool parameters."""
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pass
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@abstractmethod
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async def execute(self, **kwargs: Any) -> Any:
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"""
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Execute the tool with given parameters.
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Args:
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**kwargs: Tool-specific parameters.
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Returns:
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Result of the tool execution (string or list of content blocks).
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"""
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pass
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def cast_params(self, params: dict[str, Any]) -> dict[str, Any]:
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"""Apply safe schema-driven casts before validation."""
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schema = self.parameters or {}
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if schema.get("type", "object") != "object":
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return params
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return self._cast_object(params, schema)
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def _cast_object(self, obj: Any, schema: dict[str, Any]) -> dict[str, Any]:
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"""Cast an object (dict) according to schema."""
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if not isinstance(obj, dict):
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return obj
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props = schema.get("properties", {})
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result = {}
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for key, value in obj.items():
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if key in props:
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result[key] = self._cast_value(value, props[key])
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else:
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result[key] = value
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return result
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def _cast_value(self, val: Any, schema: dict[str, Any]) -> Any:
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"""Cast a single value according to schema."""
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target_type = self._resolve_type(schema.get("type"))
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if target_type == "boolean" and isinstance(val, bool):
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return val
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if target_type == "integer" and isinstance(val, int) and not isinstance(val, bool):
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return val
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if target_type in self._TYPE_MAP and target_type not in ("boolean", "integer", "array", "object"):
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expected = self._TYPE_MAP[target_type]
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if isinstance(val, expected):
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return val
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if target_type == "integer" and isinstance(val, str):
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try:
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return int(val)
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except ValueError:
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return val
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if target_type == "number" and isinstance(val, str):
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try:
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return float(val)
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except ValueError:
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return val
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if target_type == "string":
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return val if val is None else str(val)
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if target_type == "boolean" and isinstance(val, str):
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val_lower = val.lower()
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if val_lower in ("true", "1", "yes"):
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return True
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if val_lower in ("false", "0", "no"):
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return False
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return val
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if target_type == "array" and isinstance(val, list):
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item_schema = schema.get("items")
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return [self._cast_value(item, item_schema) for item in val] if item_schema else val
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if target_type == "object" and isinstance(val, dict):
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return self._cast_object(val, schema)
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return val
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def validate_params(self, params: dict[str, Any]) -> list[str]:
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"""Validate tool parameters against JSON schema. Returns error list (empty if valid)."""
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if not isinstance(params, dict):
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return [f"parameters must be an object, got {type(params).__name__}"]
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schema = self.parameters or {}
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if schema.get("type", "object") != "object":
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raise ValueError(f"Schema must be object type, got {schema.get('type')!r}")
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return self._validate(params, {**schema, "type": "object"}, "")
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def _validate(self, val: Any, schema: dict[str, Any], path: str) -> list[str]:
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raw_type = schema.get("type")
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nullable = (isinstance(raw_type, list) and "null" in raw_type) or schema.get(
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"nullable", False
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)
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t, label = self._resolve_type(raw_type), path or "parameter"
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if nullable and val is None:
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return []
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if t == "integer" and (not isinstance(val, int) or isinstance(val, bool)):
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return [f"{label} should be integer"]
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if t == "number" and (
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not isinstance(val, self._TYPE_MAP[t]) or isinstance(val, bool)
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):
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return [f"{label} should be number"]
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if t in self._TYPE_MAP and t not in ("integer", "number") and not isinstance(val, self._TYPE_MAP[t]):
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return [f"{label} should be {t}"]
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errors = []
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if "enum" in schema and val not in schema["enum"]:
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errors.append(f"{label} must be one of {schema['enum']}")
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if t in ("integer", "number"):
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if "minimum" in schema and val < schema["minimum"]:
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errors.append(f"{label} must be >= {schema['minimum']}")
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if "maximum" in schema and val > schema["maximum"]:
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errors.append(f"{label} must be <= {schema['maximum']}")
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if t == "string":
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if "minLength" in schema and len(val) < schema["minLength"]:
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errors.append(f"{label} must be at least {schema['minLength']} chars")
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if "maxLength" in schema and len(val) > schema["maxLength"]:
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errors.append(f"{label} must be at most {schema['maxLength']} chars")
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if t == "object":
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props = schema.get("properties", {})
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for k in schema.get("required", []):
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if k not in val:
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errors.append(f"missing required {path + '.' + k if path else k}")
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for k, v in val.items():
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if k in props:
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errors.extend(self._validate(v, props[k], path + "." + k if path else k))
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if t == "array" and "items" in schema:
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for i, item in enumerate(val):
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errors.extend(
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self._validate(item, schema["items"], f"{path}[{i}]" if path else f"[{i}]")
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)
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return errors
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def to_schema(self) -> dict[str, Any]:
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"""Convert tool to OpenAI function schema format."""
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return {
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"type": "function",
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"function": {
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"name": self.name,
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"description": self.description,
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"parameters": self.parameters,
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},
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}
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