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Fix(tools): Ensure tool schema enum compatibility for Google API #336

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Fix(tools): Ensure tool schema enum compatibility for Google API #336

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15 changes: 13 additions & 2 deletions langchain_mcp_adapters/tools.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@
This module provides functionality to convert MCP tools into LangChain-compatible
tools, handle tool execution, and manage tool conversion between the two formats.
"""

from copy import deepcopy
from typing import Any, cast, get_args

from langchain_core.tools import (
Expand Down Expand Up @@ -150,6 +150,17 @@ def convert_mcp_tool_to_langchain_tool(
msg = "Either a session or a connection config must be provided"
raise ValueError(msg)

input_schema = None
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@FerroLx thanks for the PR: The place to fix this is likely langchain_core. Could you check if the issue exists in langchain_core / if you can reproduce it there?

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yes you are correct!

the error is from the core.

it reproduces with this script

import os
from dotenv import load_dotenv
from langchain_core.tools import StructuredTool
from langchain_google_genai import ChatGoogleGenerativeAI

load_dotenv()

# This buggy schema simulates a tool definition from an external source
# where the 'type' for an enum is missing.
buggy_schema = {
    "title": "MyTool",
    "description": "A tool with a buggy enum schema.",
    "type": "object",
    "properties": {
        "param": {
            "description": "A parameter with an enum but no type.",
            "enum": ["a", "b"],
        }
    },
    "required": ["param"],
}


def my_tool_function(param: str):
    """A simple tool to demonstrate the issue."""
    return f"You selected {param}"


try:
    # We define the tool directly with the buggy dictionary schema
    tool = StructuredTool(
        name="MyTool",
        description="A tool with a buggy enum schema.",
        func=my_tool_function,
        args_schema=buggy_schema,
    )

    # Initialize the Vertex AI Chat Model
    # Make sure you are authenticated with Google Cloud CLI
    # and have set your project in the environment variables.
    # e.g., os.environ["GCLOUD_PROJECT"] = "your-gcloud-project"
    model = ChatGoogleGenerativeAI(model="gemini-2.5-flash")

    # Bind the tool to the model
    model_with_tools = model.bind_tools([tool])

    # This will raise an error when the tool schema is processed by the Google API
    print("Invoking model with tool...")
    model_with_tools.invoke("Select option a")

except Exception as e:
    print(f"Successfully reproduced the bug with langchain-google: {e}")

I've found the solution!

It's just do this!

def _convert_json_schema_to_openai_function(
    schema: dict,
    *,
    name: Optional[str] = None,
    description: Optional[str] = None,
    rm_titles: bool = True,
) -> FunctionDescription:
    """Converts a Pydantic model to a function description for the OpenAI API.

    Args:
        schema: The JSON schema to convert.
        name: The name of the function. If not provided, the title of the schema will be
            used.
        description: The description of the function. If not provided, the description
            of the schema will be used.
        rm_titles: Whether to remove titles from the schema. Defaults to True.

    Returns:
        The function description.
    """
    schema = dereference_refs(schema)
    if "definitions" in schema:  # pydantic 1
        schema.pop("definitions", None)
    if "$defs" in schema:  # pydantic 2
        schema.pop("$defs", None)

    # FIX: Ensure that any property with an 'enum' also has a 'type'
    if "properties" in schema:
        for prop in schema["properties"].values():
            if "enum" in prop and "type" not in prop:
                prop["type"] = "string"

    title = schema.pop("title", "")
    default_description = schema.pop("description", "")
    return {
        "name": name or title,
        "description": description or default_description,
        "parameters": _rm_titles(schema) if rm_titles else schema,
    }

Do you want me to do a pr for it or you'll do it?

@eyurtsev

if isinstance(tool.inputSchema, dict):
input_schema = deepcopy(tool.inputSchema)
if "properties" in input_schema:
for _, prop_schema in input_schema.get("properties", {}).items():
if "enum" in prop_schema and "type" not in prop_schema:
prop_schema["type"] = "string"
else:
input_schema = tool.inputSchema


async def call_tool(
**arguments: dict[str, Any],
) -> tuple[str | list[str], list[NonTextContent] | None]:
Expand Down Expand Up @@ -255,7 +266,7 @@ async def call_tool(
return StructuredTool(
name=tool.name,
description=tool.description or "",
args_schema=tool.inputSchema,
args_schema=input_schema,
coroutine=call_tool,
response_format="content_and_artifact",
metadata=metadata,
Expand Down