How do I get my Client ID and API Key for Frontegg?

Log in to the Frontegg Portal, select your workspace/environment, go to 'Environment Settings' > 'API Keys', and create a Vendor token to get your Client ID and API Key.

Does this support multi-tenant management?

Yes! The agent can list, create, and delete individual tenants (customer accounts) within your environment using the 'list_tenants' and 'create_tenant' tools.

Can I search for a user across all tenants?

Yes, use the 'list_users' tool and provide an exact 'email' parameter. The agent will search across all tenants in the environment to find the user profile.

Is it possible to view role permissions?

You can use the 'list_system_roles' to view all roles and the 'list_permissions' tool to see the granular permissions available for assignment.

Frontegg MCP Server for LlamaIndex 12 tools — connect in under 2 minutes

Name: Frontegg
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 12 Tools Framework

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

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

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 Frontegg. "
            "You have 12 tools available."
        ),
    )

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

asyncio.run(main())

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* 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 Frontegg MCP Server

Connect your Frontegg environment to any AI agent to automate your B2B SaaS identity management through the Model Context Protocol (MCP). Frontegg is a powerful user management and authentication platform designed specifically for modern SaaS applications. This MCP server enables you to manage multi-tenant architectures, provision new users, and audit security configurations directly through natural conversation.

LlamaIndex agents combine Frontegg tool responses with indexed documents for comprehensive, grounded answers. Connect 12 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.

Key Features

Tenant Orchestration — List all customer accounts (tenants), retrieve their configuration details, and programmatically create or delete tenants.
User Provisioning — Access your global user database, fetch detailed profiles across tenants, and instantly invite or remove users.
Role & Permission Discovery — List all system roles and granular permissions to audit your security and access control models.
M2M Token Management — Retrieve Machine-to-Machine tokens for specific tenants to simplify backend integrations.
Real-time Synchronization — Keep your identity and access management operations accessible to your AI assistant without leaving your primary workspace.
Secure Environment Access — Authenticate securely using Vendor Client ID and API Keys to perform administrative operations safely.

The Frontegg MCP Server exposes 12 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 Frontegg to LlamaIndex via MCP

Follow these steps to integrate the Frontegg 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 12 tools from Frontegg

Why Use LlamaIndex with the Frontegg MCP Server

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

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

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

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

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

Frontegg + LlamaIndex Use Cases

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

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

Data enrichment: query Frontegg 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 Frontegg for fresh data

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

Frontegg MCP Tools for LlamaIndex (12)

These 12 tools become available when you connect Frontegg to LlamaIndex via MCP:

check_environment_status

Verify API connection

create_tenant

Create a new tenant

create_user

Provision a user

delete_tenant

Delete a tenant

delete_user

Remove a user

get_tenant_details

Get tenant metadata

get_user_details

Get user metadata

list_m2m_tokens

List machine tokens

list_permissions

List granular permissions

list_system_roles

g. Admin, Read-Only) available for assignment. List roles

list_tenants

List all tenants/accounts

list_users

List users globally

Example Prompts for Frontegg in LlamaIndex

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

"List the first 10 tenants in our Frontegg environment."

"Find the user details for 'jane@example.com'."

"Create a new tenant named 'Stark Industries'."

Troubleshooting Frontegg MCP Server with LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Frontegg + LlamaIndex FAQ

Common questions about integrating Frontegg 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 Frontegg tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.