diff --git a/langchain_mcp_adapters/tools.py b/langchain_mcp_adapters/tools.py
index 2cdc6d4..af904a5 100644
--- a/langchain_mcp_adapters/tools.py
+++ b/langchain_mcp_adapters/tools.py
@@ -4,7 +4,7 @@
 tools, handle tool execution, and manage tool conversion between the two formats.
 """
 
-from typing import Any, cast, get_args
+from typing import Annotated, Any, Literal, Union, cast, get_args
 
 from langchain_core.tools import (
     BaseTool,
@@ -146,6 +146,65 @@ async def call_tool(
 
         return _convert_call_tool_result(call_tool_result)
 
+    # base types being mapped from JSON
+    type_map = {
+        "null": None,
+        "integer": int,
+        "float": float,
+        "string": str,
+        "bool": bool,
+        "object": dict,
+        "bytes": bytes,
+    }
+
+    def _parse_model_fields(args: dict, injected_state: str | None = None) -> dict:
+        """Parse a JSON field into a Pydantic Field, taking into account injected state.
+
+        :param args: the function parameter schema
+        :type args: dict
+        :param injected_state: the name of the key used for the InjectedState
+        :type injected_state: str
+        :return: returns a dict of fields with their pydantic type
+        and default value if any
+        :rtype: dict
+        """
+        model_fields = {}
+
+        def _parse_field(props: dict) -> tuple[type, Any]:
+            if "anyOf" in props:
+                return Union[tuple(_parse_field(p) for p in props["anyOf"])]  # noqa: UP007
+            if "enum" in props:
+                return Literal[tuple(props["enum"])]
+            if props["type"] == "array":
+                items = props["items"]
+                return list[_parse_field(items)]
+            return type_map.get(props["type"], dict)
+
+        for field, props in args["properties"].items():
+            if field == injected_state:
+                field_type = Annotated[dict, InjectedState]
+            else:
+                field_type = _parse_field(props)
+            if "default" in props:
+                default = props["default"]
+                model_fields[field] = (field_type, default)
+            else:
+                model_fields[field] = (field_type, ...)
+        return model_fields
+
+    args = tool.inputSchema
+    # check for the `injected_state`` annotation on the MCP tool.
+    # The injected_state value is the name of the function parameter used
+    # as the injected state
+    injected_state = tool.annotations.model_extra.get("injected_state")
+    if injected_state:
+        # import langgraph InjectedState only if we need it
+        from langgraph.prebuilt import InjectedState
+    model_fields = _parse_model_fields(args, injected_state)
+
+    # recreate a dynamic model based on the parsed JSON schema,
+    # which will be properly parsed with annotations for the InjectedState
+    args_schema = create_model(tool.name, **model_fields)
     meta = tool.meta if hasattr(tool, "meta") else None
     base = tool.annotations.model_dump() if tool.annotations is not None else {}
     meta = {"_meta": meta} if meta is not None else {}
@@ -154,7 +213,7 @@ async def call_tool(
     return StructuredTool(
         name=tool.name,
         description=tool.description or "",
-        args_schema=tool.inputSchema,
+        args_schema=args_schema,
         coroutine=call_tool,
         response_format="content_and_artifact",
         metadata=metadata,