How to Use the ToolJet MCP in LangChain

Q: Can LangChain handle complex queries using ToolJet?

Absolutely. The querytable tool lets your chain run full SELECT statements against the database. You'll get structured results back that you can process in subsequent steps.

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Connect ToolJet MCP to LangChain

Create your Vinkius account to connect ToolJet to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup ToolJet with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Database Operations in a Chain

The `query_table` tool lets your agent read specific data points from a ToolJet Database table. It executes SQL SELECT queries, so the output is structured information that immediately feeds into the next step of your chain. Need to change things? Use `insert_row` to write new records directly back to the database, making your multi-step pipeline fully actionable.

Automating Workflows

You can trigger processes outside the agent using the `trigger_workflow` tool. This sends a webhook call that starts an entire external automation process right when your chain decides it's time. It’s perfect for building pipelines where one decision point needs to kick off another system, like notifying a user or updating a CRM record.

Database Schema Discovery

Don't know what data is available? The `list_tables` tool shows all the tables in your connected ToolJet Database. Your agent can check this list first, figuring out exactly which datasets it needs to query. This makes the agent self-correcting; if it gets stuck, it just asks the MCP Server for a list of available resources.

Setup guide

Set up ToolJet MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes ToolJet tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "tooljet-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent ToolJet transactions"
    })
    print(result["messages"][-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by ToolJet. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about ToolJet MCP in LangChain

You pass the ToolJet tools to your ReAct agent. Your AI client figures out when it needs to read or write data, and then calls the specific function automatically.

Absolutely. The `query_table` tool lets your chain run full SELECT statements against the database. You'll get structured results back that you can process in subsequent steps.

ToolJet manages records and tables within its connected databases. When using ToolJet with your agent, the system touches structured database data types.

Yeah, it does. Your chain can decide to run a whole external workflow by calling `trigger_workflow`. This is how you connect your agent logic to other services.

The tool calls are synchronous, meaning your chain gets immediate feedback. You're always working with real database records retrieved via `query_table` or new rows inserted by `insert_row`.

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