Retool MCP Server for LlamaIndex 7 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Retool as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
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 Retool. "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in Retool?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Retool MCP Server
Connect your conversational assistant directly to the Retool ecosystem. This integration enables your AI to explore the organizational structure of your internal tools, auditing who has access to what, and reviewing which databases are connected.
LlamaIndex agents combine Retool tool responses with indexed documents for comprehensive, grounded answers. Connect 7 tools through the 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
- Audit Applications — Ask your assistant to scan your Retool workspace (
list_apps) and drill down into the configuration of specific tools (get_app). Observe how tools are organized by requesting a view of the folder hierarchy (list_folders). - Manage Permissions & Users — Review the active members of your Retool organization (
list_users) and understand their access levels by listing the existing permission groups (list_groups). - Review DevOps & Infrastructure — Command the AI to inspect which data sources or APIs are wired into your operational stack (
list_resources), and list any active background automation tasks (list_workflows).
The Retool MCP Server exposes 7 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Retool to LlamaIndex via MCP
Follow these steps to integrate the Retool MCP Server with LlamaIndex.
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 7 tools from Retool
Why Use LlamaIndex with the Retool MCP Server
LlamaIndex provides unique advantages when paired with Retool through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Retool tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Retool tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Retool, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Retool tools were called, what data was returned, and how it influenced the final answer
Retool + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Retool MCP Server delivers measurable value.
Hybrid search: combine Retool real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Retool 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 Retool for fresh data
Analytical workflows: chain Retool queries with LlamaIndex's data connectors to build multi-source analytical reports
Retool MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect Retool to LlamaIndex via MCP:
get_app
Retrieves details for a specific Retool application
list_apps
Lists all applications in the Retool organization
list_folders
Lists all folders in the Retool workspace
list_groups
Lists all permission groups
list_resources
Lists all data resources configured in Retool
list_users
Lists all users in the Retool organization
list_workflows
Lists all Retool Workflows
Example Prompts for Retool in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Retool immediately.
"List all users in my Retool workspace."
"List all applications currently configured."
"Tell me what resources are connected to our Retool."
Troubleshooting Retool MCP Server with LlamaIndex
Common issues when connecting Retool to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpRetool + LlamaIndex FAQ
Common questions about integrating Retool MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Retool with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Retool to LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.