Python: added more complete parsing for mcp tool arguments

python/packages/core/agent_framework/_mcp.py

@@ -1,6 +1,5 @@
 # Copyright (c) Microsoft. All rights reserved.
 
-import json
 import logging
 import re
 import sys
@@ -19,9 +18,9 @@
 from mcp.shared.context import RequestContext
 from mcp.shared.exceptions import McpError
 from mcp.shared.session import RequestResponder
-from pydantic import BaseModel, Field, create_model
+from pydantic import BaseModel, create_model
 
-from ._tools import AIFunction, HostedMCPSpecificApproval
+from ._tools import AIFunction, HostedMCPSpecificApproval, _build_pydantic_model_from_json_schema
 from ._types import (
     ChatMessage,
     Contents,
@@ -274,95 +273,26 @@ def _get_input_model_from_mcp_prompt(prompt: types.Prompt) -> type[BaseModel]:
     if not prompt.arguments:
         return create_model(f"{prompt.name}_input")
 
-    field_definitions: dict[str, Any] = {}
-    for prompt_argument in prompt.arguments:
-        # For prompts, all arguments are typically required and string type
-        # unless specified otherwise in the prompt argument
-        python_type = str  # Default type for prompt arguments
+    # Convert prompt arguments to JSON schema format
+    properties: dict[str, Any] = {}
+    required: list[str] = []
 
-        # Create field definition for create_model
+    for prompt_argument in prompt.arguments:
+        # For prompts, all arguments are typically string type unless specified otherwise
+        properties[prompt_argument.name] = {
+            "type": "string",
+            "description": prompt_argument.description if hasattr(prompt_argument, "description") else "",
+        }
         if prompt_argument.required:
-            field_definitions[prompt_argument.name] = (python_type, ...)
-        else:
-            field_definitions[prompt_argument.name] = (python_type, None)
+            required.append(prompt_argument.name)
 
-    return create_model(f"{prompt.name}_input", **field_definitions)
+    schema = {"properties": properties, "required": required}
+    return _build_pydantic_model_from_json_schema(prompt.name, schema)
 
 
 def _get_input_model_from_mcp_tool(tool: types.Tool) -> type[BaseModel]:
     """Creates a Pydantic model from a tools parameters."""
-    properties = tool.inputSchema.get("properties", None)
-    required = tool.inputSchema.get("required", [])
-    definitions = tool.inputSchema.get("$defs", {})
-
-    # Check if 'properties' is missing or not a dictionary
-    if not properties:
-        return create_model(f"{tool.name}_input")
-
-    def resolve_type(prop_details: dict[str, Any]) -> type:
-        """Resolve JSON Schema type to Python type, handling $ref."""
-        # Handle $ref by resolving the reference
-        if "$ref" in prop_details:
-            ref = prop_details["$ref"]
-            # Extract the reference path (e.g., "#/$defs/CustomerIdParam" -> "CustomerIdParam")
-            if ref.startswith("#/$defs/"):
-                def_name = ref.split("/")[-1]
-                if def_name in definitions:
-                    # Resolve the reference and use its type
-                    resolved = definitions[def_name]
-                    return resolve_type(resolved)
-            # If we can't resolve the ref, default to dict for safety
-            return dict
-
-        # Map JSON Schema types to Python types
-        json_type = prop_details.get("type", "string")
-        match json_type:
-            case "integer":
-                return int
-            case "number":
-                return float
-            case "boolean":
-                return bool
-            case "array":
-                return list
-            case "object":
-                return dict
-            case _:
-                return str  # default
-
-    field_definitions: dict[str, Any] = {}
-    for prop_name, prop_details in properties.items():
-        prop_details = json.loads(prop_details) if isinstance(prop_details, str) else prop_details
-
-        python_type = resolve_type(prop_details)
-        description = prop_details.get("description", "")
-
-        # Build field kwargs (description, array items schema, etc.)
-        field_kwargs: dict[str, Any] = {}
-        if description:
-            field_kwargs["description"] = description
-
-        # Preserve array items schema if present
-        if prop_details.get("type") == "array" and "items" in prop_details:
-            items_schema = prop_details["items"]
-            if items_schema and items_schema != {}:
-                field_kwargs["json_schema_extra"] = {"items": items_schema}
-
-        # Create field definition for create_model
-        if prop_name in required:
-            if field_kwargs:
-                field_definitions[prop_name] = (python_type, Field(**field_kwargs))
-            else:
-                field_definitions[prop_name] = (python_type, ...)
-        else:
-            default_value = prop_details.get("default", None)
-            field_kwargs["default"] = default_value
-            if field_kwargs and any(k != "default" for k in field_kwargs):
-                field_definitions[prop_name] = (python_type, Field(**field_kwargs))
-            else:
-                field_definitions[prop_name] = (python_type, default_value)
-
-    return create_model(f"{tool.name}_input", **field_definitions)
+    return _build_pydantic_model_from_json_schema(tool.name, tool.inputSchema)
 
 
 def _normalize_mcp_name(name: str) -> str:

python/packages/core/agent_framework/_tools.py

@@ -25,7 +25,6 @@
 
 from opentelemetry.metrics import Histogram
 from pydantic import AnyUrl, BaseModel, Field, ValidationError, create_model
-from pydantic.fields import FieldInfo
 
 from ._logging import get_logger
 from ._serialization import SerializationMixin
@@ -932,6 +931,151 @@ def _create_input_model_from_func(func: Callable[..., Any], name: str) -> type[B
 }
 
 
+def _build_pydantic_model_from_json_schema(
+    model_name: str,
+    schema: Mapping[str, Any],
+) -> type[BaseModel]:
+    """Creates a Pydantic model from JSON Schema with support for $refs, nested objects, and typed arrays.
+
+    Args:
+        model_name: The name of the model to be created.
+        schema: The JSON Schema definition (should contain 'properties', 'required', '$defs', etc.).
+
+    Returns:
+        The dynamically created Pydantic model class.
+    """
+    properties = schema.get("properties")
+    required = schema.get("required", [])
+    definitions = schema.get("$defs", {})
+
+    # Check if 'properties' is missing or not a dictionary
+    if not properties:
+        return create_model(f"{model_name}_input")
+
+    def _resolve_type(prop_details: dict[str, Any], parent_name: str = "") -> type:
+        """Resolve JSON Schema type to Python type, handling $ref, nested objects, and typed arrays.
+
+        Args:
+            prop_details: The JSON Schema property details
+            parent_name: Name to use for creating nested models (for uniqueness)
+
+        Returns:
+            Python type annotation (could be int, str, list[str], or a nested Pydantic model)
+        """
+        # Handle $ref by resolving the reference
+        if "$ref" in prop_details:
+            ref = prop_details["$ref"]
+            # Extract the reference path (e.g., "#/$defs/CustomerIdParam" -> "CustomerIdParam")
+            if ref.startswith("#/$defs/"):
+                def_name = ref.split("/")[-1]
+                if def_name in definitions:
+                    # Resolve the reference and use its type
+                    resolved = definitions[def_name]
+                    return _resolve_type(resolved, def_name)
+            # If we can't resolve the ref, default to dict for safety
+            return dict
+
+        # Map JSON Schema types to Python types
+        json_type = prop_details.get("type", "string")
+        match json_type:
+            case "integer":
+                return int
+            case "number":
+                return float
+            case "boolean":
+                return bool
+            case "array":
+                # Handle typed arrays
+                items_schema = prop_details.get("items")
+                if items_schema and isinstance(items_schema, dict):
+                    # Recursively resolve the item type
+                    item_type = _resolve_type(items_schema, f"{parent_name}_item")
+                    # Return list[ItemType] instead of bare list
+                    return list[item_type]  # type: ignore
+                # If no items schema or invalid, return bare list
+                return list
+            case "object":
+                # Handle nested objects by creating a nested Pydantic model
+                nested_properties = prop_details.get("properties")
+                nested_required = prop_details.get("required", [])
+
+                if nested_properties and isinstance(nested_properties, dict):
+                    # Create the name for the nested model
+                    nested_model_name = f"{parent_name}_nested" if parent_name else "NestedModel"
+
+                    # Recursively build field definitions for the nested model
+                    nested_field_definitions: dict[str, Any] = {}
+                    for nested_prop_name, nested_prop_details in nested_properties.items():
+                        nested_prop_details = (
+                            json.loads(nested_prop_details)
+                            if isinstance(nested_prop_details, str)
+                            else nested_prop_details
+                        )
+
+                        nested_python_type = _resolve_type(
+                            nested_prop_details, f"{nested_model_name}_{nested_prop_name}"
+                        )
+                        nested_description = nested_prop_details.get("description", "")
+
+                        # Build field kwargs for nested property
+                        nested_field_kwargs: dict[str, Any] = {}
+                        if nested_description:
+                            nested_field_kwargs["description"] = nested_description
+
+                        # Create field definition
+                        if nested_prop_name in nested_required:
+                            nested_field_definitions[nested_prop_name] = (
+                                (
+                                    nested_python_type,
+                                    Field(**nested_field_kwargs),
+                                )
+                                if nested_field_kwargs
+                                else (nested_python_type, ...)
+                            )
+                        else:
+                            nested_field_kwargs["default"] = nested_prop_details.get("default", None)
+                            nested_field_definitions[nested_prop_name] = (
+                                nested_python_type,
+                                Field(**nested_field_kwargs),
+                            )
+
+                    # Create and return the nested Pydantic model
+                    return create_model(nested_model_name, **nested_field_definitions)  # type: ignore
+
+                # If no properties defined, return bare dict
+                return dict
+            case _:
+                return str  # default
+
+    field_definitions: dict[str, Any] = {}
+    for prop_name, prop_details in properties.items():
+        prop_details = json.loads(prop_details) if isinstance(prop_details, str) else prop_details
+
+        python_type = _resolve_type(prop_details, f"{model_name}_{prop_name}")
+        description = prop_details.get("description", "")
+
+        # Build field kwargs (description, etc.)
+        field_kwargs: dict[str, Any] = {}
+        if description:
+            field_kwargs["description"] = description
+
+        # Create field definition for create_model
+        if prop_name in required:
+            if field_kwargs:
+                field_definitions[prop_name] = (python_type, Field(**field_kwargs))
+            else:
+                field_definitions[prop_name] = (python_type, ...)
+        else:
+            default_value = prop_details.get("default", None)
+            field_kwargs["default"] = default_value
+            if field_kwargs and any(k != "default" for k in field_kwargs):
+                field_definitions[prop_name] = (python_type, Field(**field_kwargs))
+            else:
+                field_definitions[prop_name] = (python_type, default_value)
+
+    return create_model(f"{model_name}_input", **field_definitions)
+
+
 def _create_model_from_json_schema(tool_name: str, schema_json: Mapping[str, Any]) -> type[BaseModel]:
     """Creates a Pydantic model from a given JSON Schema.
 
@@ -948,29 +1092,8 @@ def _create_model_from_json_schema(tool_name: str, schema_json: Mapping[str, Any
             f"JSON schema for tool '{tool_name}' must contain a 'properties' key of type dict. "
             f"Got: {schema_json.get('properties', None)}"
         )
-    # Extract field definitions with type annotations
-    field_definitions: dict[str, tuple[type, FieldInfo]] = {}
-    for field_name, field_schema in schema_json["properties"].items():
-        field_args: dict[str, Any] = {}
-        if (field_description := field_schema.get("description", None)) is not None:
-            field_args["description"] = field_description
-        if (field_default := field_schema.get("default", None)) is not None:
-            field_args["default"] = field_default
-        field_type = field_schema.get("type", None)
-        if field_type is None:
-            raise ValueError(
-                f"Missing 'type' for field '{field_name}' in JSON schema. "
-                f"Got: {field_schema}, Supported types: {list(TYPE_MAPPING.keys())}"
-            )
-        python_type = TYPE_MAPPING.get(field_type)
-        if python_type is None:
-            raise ValueError(
-                f"Unsupported type '{field_type}' for field '{field_name}' in JSON schema. "
-                f"Got: {field_schema}, Supported types: {list(TYPE_MAPPING.keys())}"
-            )
-        field_definitions[field_name] = (python_type, Field(**field_args))
 
-    return create_model(f"{tool_name}_input", **field_definitions)  # type: ignore[call-overload, no-any-return]
+    return _build_pydantic_model_from_json_schema(tool_name, schema_json)
 
 
 @overload

python/packages/core/pyproject.toml

@@ -35,7 +35,7 @@ dependencies = [
     # connectors and functions
     "openai>=1.99.0",
     "azure-identity>=1,<2",
-    "mcp[ws]>=1.13",
+    "mcp[ws]>=1.23",
     "packaging>=24.1",
 ]