Fix Gemini "400 Error" with LangChain.js + MCP

Drop-in replacement that unblocks MCP tool schemas in Gemini

This library provides a drop-in replacement for @langchain/google-genai that fixes Gemini's 400 Bad Request errors when using LangChain.js with MCP servers. Automatically transforms schemas with unsupported constructs (e.g., anyOf, allOf) into Gemini-compatible JSON.

The schema error usually looks like:
[GoogleGenerativeAI Error]: ... [400 Bad Request] Invalid JSON payload received.

This error typically occurs when using MultiServerMCPClient().
This library prevents its cascading failures where one complex server breaks the entire MCP integration.

How to Use This Library

Drop-in Replacement

Replace:

import { ChatGoogleGenerativeAI } from '@langchain/google-genai';
...
const llm = new ChatGoogleGenerativeAI({ ... });

with:

import { ChatGoogleGenerativeAIEx } from '@h1deya/langchain-google-genai-ex';
...
const llm = new ChatGoogleGenerativeAIEx({ ... });

That's it! No configuration, no additional steps.

This automatically fixes:

• ✅ "anyOf must be the only field set" errors
• ✅ "Unknown name 'exclusiveMaximum'" schema validation errors
• ✅ "Invalid JSON payload" errors from complex MCP schemas
• ✅ Cascading failures where one complex server breaks entire MCP integration
• ✅ Works with both Gemini 1.5 and 2.5

You can easily switch back to the original ChatGoogleGenerativeAI when its schema handling improves, or when the MCP server's schema improves to meet Gemini's strict requirements.

A simple usage example, which is ready to clone and run, can be found here.

This library addresses compatibility issues present as of September 5, 2025, with LangChain.js (@langchain/core) v0.3.72 and @langchain/google-genai v0.2.16.

Table of Contents

Below we'll explain what and how this library works in detail:

• Prerequisites
• Installation
• The Problem You're Probably Having
• Complete Usage Example
• Debugging: Verbose Logging
• Features
• API Reference

Prerequisites

Before installing, make sure you have:

• Node.js 18+ - Required for modern JavaScript features
• Google API Key - Get yours at Google AI Studio
• LangChain.js - This package works with @langchain/core and @langchain/mcp-adapters
• MCP Servers - Access to the MCP servers you want to use

Tested with @langchain/[email protected] and @langchain/[email protected].

Installation

npm i @h1deya/langchain-google-genai-ex

The Problem You're Probably Having

If you've ever tried using Google Gemini together with LangChain.js and MCP servers with complex schemas, you may have run into this error:

[GoogleGenerativeAI Error]: Error fetching from 
https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent: 
[400 Bad Request] Invalid JSON payload received. 
Unknown name "anyOf" at 'tools[0].function_declarations[8]...

This typically occurs when you configure multiple MCP servers using MultiServerMCPClient, especially when some of the servers have complex schemas.

If you searched for GoogleGenerativeAIFetchError: [GoogleGenerativeAI Error] 400 Bad Request, the following sections explain the cause and how to workaround it when using LangChain.

The MCP servers I have encountered so far that have failed are:

• @notionhq/[email protected] (run with npx)
• [email protected] (run with npx)
• mcp-server-fetch==2025.4.7 (run with uvx)

Why This Happens

• Gemini's schema requirements for function calling are very strict.
• MCP servers define their tools using flexible JSON schemas, and LLMs invoke MCP tools via function calling. Most LLMs accept these schemas just fine.
• However, Gemini API rejects MCP tool schemas if they contain fields it doesn't expect (e.g., use of anyOf).
• The result is a 400 Bad Request - even though the same MCP server works fine with OpenAI, Anthropic, etc.
• Google Vertex AI that supports API endpoints with relaxed schema requirements but it requires GCP setup.
• Google provides a fix in its new Gemini SDK (@google/genai), but LangChain.js users cannot leverage it due to architectural incompatibility.

For many developers, this can make Gemini difficult to use with LangChain.js and some MCP servers. Even if only one incompatible MCP server is included in the MCP definitions passed to MultiServerMCPClient, all subsequent MCP usage starts failing with the 400 Bad Request error.

This library handles all these schema incompatibilities through schema transformation, converting complex MCP tool schemas into Gemini-friendly formats, so you can focus on building instead of debugging schema errors.

Complete Usage Example

// import { ChatGoogleGenerativeAI } from "@langchain/google-genai";
import { ChatGoogleGenerativeAIEx } from '@h1deya/langchain-google-genai-ex';
import { createReactAgent } from '@langchain/langgraph/prebuilt';
import { MultiServerMCPClient } from '@langchain/mcp-adapters';
import { HumanMessage } from '@langchain/core/messages';

// The following Fetch MCP server causes "400 Bad Request"
const client = new MultiServerMCPClient({
  mcpServers: {
    fetch: {
      command: "uvx",
      args: ["mcp-server-fetch==2025.4.7"]
    }
  }
});

const mcpTools = await client.getTools();

// const llm = new ChatGoogleGenerativeAI({ model: "gemini-2.5-flash" });
const llm = new ChatGoogleGenerativeAIEx({ model: "gemini-2.5-flash"} );

const agent = createReactAgent({ llm, tools: mcpTools });

// This works! No more schema errors
const result = await agent.invoke({
  messages: [new HumanMessage("Read the top news headlines on bbc.com")]
});

console.log(result.messages[result.messages.length - 1].content);
await client.close();

A simple usage example, which is ready to clone and run, can be found here.

Key Benefits:

• Simple to use - Just replace the import and the classname
• Preserves all functionality - Streaming, system instructions, etc.
• No breaking changes - Drop-in replacement for ChatGoogleGenerativeAI

Debugging: Verbose Logging

Want to see exactly what schema transformations are happening? Set the environment variable to get detailed logs:

LANGCHAIN_GOOGLE_GENAI_EX_VERBOSE=true

Example output:

🔧 Transforming 3 MCP tool(s) for Gemini compatibility...
  ✅ get-alerts: No transformation needed (simple schema)
  ✅ get-forecast: No transformation needed (simple schema)
  🔄 fetch: 2 exclusive bound(s) converted, 1 unsupported format(s) removed (uri)
📊 Summary: 1/3 tool(s) required schema transformation

When to use verbose logging:

• Debugging: When tools aren't working as expected
• Understanding: See what complex schemas are being simplified
• Verification: Confirm that transformations are happening correctly
• Development: Monitor which MCP servers need schema fixes

The verbose output helps you understand:

• Which tools have complex schemas that need transformation
• What specific changes are being made (anyOf fixes, type conversions, etc.)
• How many tools in your setup require compatibility fixes

Features

All Original ChatGoogleGenerativeAI Features

ChatGoogleGenerativeAIEx extends the original class, so you get everything:

• Streaming, function calling, system instructions
• All model parameters and configurations
• Full LangChain.js integration

Comprehensive Schema Transformation

• Systematic conversion of allOf/anyOf/oneOf to equivalent object structures
• Reference resolution - handles $ref and $defs by flattening definitions
• Type normalization - converts type arrays ["string", "null"] to nullable properties
• Property validation - filters required fields that don't exist in properties
• Format compatibility - removes unsupported JSON Schema formats and keywords
• Nested structure handling - recursively processes complex object hierarchies

Known Limitations

• Unresolved references: If a schema points to $ref definitions that aren't available, they're simplified to a generic object.
• Tuple-style arrays: For schemas that define arrays with position-specific types, only the first item is used.
• Enums and formats: Only string enums and a small set of formats are kept; others are dropped.
• Complex combinations: oneOf/allOf are simplified, which may loosen or slightly change validation rules.

These adjustments keep most MCP tools working, but rare edge cases could behave differently from the original schema. Please report issues you encounter using Issue.

See this design decision document for the implementation details.

API Reference

Can be found here

Links

• A simple usage example which is ready to clone and run
• Design decision document describes the implementation details

License

MIT