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 CrewAI 12 tools — connect in under 2 minutes

Name: Frontegg
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 12 Tools Framework

Connect your CrewAI agents to Frontegg through Vinkius, pass the Edge URL in the `mcps` parameter and every Frontegg tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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

from crewai import Agent, Task, Crew

agent = Agent(
    role="Frontegg Specialist",
    goal="Help users interact with Frontegg effectively",
    backstory=(
        "You are an expert at leveraging Frontegg tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Frontegg "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 12 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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.

When paired with CrewAI, Frontegg becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Frontegg tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

Follow these steps to integrate the Frontegg MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 12 tools from Frontegg

Why Use CrewAI with the Frontegg MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Frontegg through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Frontegg + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Frontegg for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Frontegg, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Frontegg tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Frontegg against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Frontegg MCP Tools for CrewAI (12)

These 12 tools become available when you connect Frontegg to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

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

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Frontegg + CrewAI FAQ

Common questions about integrating Frontegg MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.