ToolJet MCP Server for LlamaIndexGive LlamaIndex instant access to 4 tools to Insert Row, List Tables, Query Table, and more

Name: ToolJet
Price: 29 USD
Availability: InStock
Rating: 5.0 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add ToolJet as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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The ToolJet MCP Server for LlamaIndex is a standout in the Developer Tools category — giving your AI agent 4 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

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python

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to ToolJet. "
            "You have 4 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in ToolJet?"
    )
    print(response)

asyncio.run(main())

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About ToolJet MCP Server

Connect your ToolJet workspace to any AI agent to interact with your internal databases and automate business logic through natural language.

LlamaIndex agents combine ToolJet tool responses with indexed documents for comprehensive, grounded answers. Connect 4 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

What you can do

Database Exploration — List all available tables within your ToolJet Database to understand your data structure.
Advanced Querying — Execute SQL SELECT queries on your tables to retrieve specific records, filter data, and generate insights.
Data Entry — Insert new rows into your database tables directly from the chat interface using structured JSON data.
Workflow Automation — Trigger complex ToolJet Workflows via webhooks, passing custom payloads to start automated business processes.

The ToolJet MCP Server exposes 4 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 4 ToolJet tools available for LlamaIndex

When LlamaIndex connects to ToolJet through Vinkius, your AI agent gets direct access to every tool listed below — spanning low-code, internal-tools, sql-query, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

insert

Insert row on ToolJet

Insert a new row into a ToolJet Database table

list

List tables on ToolJet

List all tables in the ToolJet Database

query

Query table on ToolJet

Use this for reading data (SELECT). Perform a SQL query on a ToolJet Database table

trigger

Trigger workflow on ToolJet

Trigger a ToolJet Workflow via Webhook

Connect ToolJet to LlamaIndex via MCP

Follow these steps to wire ToolJet into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 4 tools from ToolJet

Why Use LlamaIndex with the ToolJet MCP Server

LlamaIndex provides unique advantages when paired with ToolJet through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine ToolJet tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain ToolJet tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query ToolJet, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what ToolJet tools were called, what data was returned, and how it influenced the final answer

ToolJet + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the ToolJet MCP Server delivers measurable value.

Hybrid search: combine ToolJet real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query ToolJet to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying ToolJet for fresh data

Analytical workflows: chain ToolJet queries with LlamaIndex's data connectors to build multi-source analytical reports

Example Prompts for ToolJet in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with ToolJet immediately.

"List all the tables available in my ToolJet Database."

"Run a query to find all orders with a status of 'pending' in organization 'org_123'."

"Trigger the workflow 'wf_987' with the payload {"user_id": "123", "action": "reset"}."

Troubleshooting ToolJet MCP Server with LlamaIndex

Common issues when connecting ToolJet to LlamaIndex through Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

ToolJet + LlamaIndex FAQ

Common questions about integrating ToolJet MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query ToolJet tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.