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 Pydantic AI 12 tools — connect in under 2 minutes

Name: Frontegg
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 12 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Frontegg through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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 pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Frontegg "
            "(12 tools)."
        ),
    )

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

asyncio.run(main())

Fully ManagedVinkius Servers

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High SecurityEnterprise-grade

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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.

Pydantic AI validates every Frontegg tool response against typed schemas, catching data inconsistencies at build time. Connect 12 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI via MCP

Follow these steps to integrate the Frontegg MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

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 with type-safe schemas

Why Use Pydantic AI with the Frontegg MCP Server

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

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Frontegg integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Frontegg connection logic from agent behavior for testable, maintainable code

Frontegg + Pydantic AI Use Cases

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

Type-safe data pipelines: query Frontegg with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Frontegg tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Frontegg and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Frontegg responses and write comprehensive agent tests

Frontegg MCP Tools for Pydantic AI (12)

These 12 tools become available when you connect Frontegg to Pydantic AI 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 Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Frontegg + Pydantic AI FAQ

Common questions about integrating Frontegg MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Frontegg MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.