How to Use the Frontegg MCP in LlamaIndex

Q: How do I load Frontegg data into LlamaIndex using this MCP integration?

Install llama-index-tools-mcp and configure a BasicMCPClient. Wrap it in McpToolSpec and call the async tool list method to pass it to your FunctionAgent.

Q: Can I index tenant metadata from Frontegg using LlamaIndex?

Yes. You can configure your agent to periodically run gettenantdetails and embed the resulting JSON directly into your vector store for semantic search.

Q: How do I restrict which MCP tools the agent can use?

Use the allowedtools filter when initializing your agent. This prevents the system from accidentally executing destructive actions like deletetenant during routine data syncs.

Index your Frontegg identity data into searchable vector stores using LlamaIndex.

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Connect Frontegg MCP to LlamaIndex

Create your Vinkius account to connect Frontegg to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Frontegg with LlamaIndex

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Query the Frontegg MCP Server.

The `list_tenants` and `list_users` tools feed raw identity data directly into your LlamaIndex knowledge base. You can pull thousands of B2B accounts and index them for semantic search alongside your internal documentation. When a support rep asks who owns a specific account, the RAG application does not guess. It retrieves the exact metadata from the vector store and provides an answer grounded in your actual API state.

Map roles to RAG workflows.

Using `list_system_roles` and `list_permissions`, your application builds a searchable map of your entire access control structure. You can query this index to understand exactly what a specific admin tier can do. This turns static security policies into an interactive query engine. Your agent cross-references the indexed permissions against live data from `get_user_details` to audit access levels instantly.

Track machine token usage.

Your agent can run `list_m2m_tokens` to pull active service credentials and embed them into a monitoring index. This allows developers to search through active integrations using natural language. This MCP connection turns transient checks into a persistent record. If you need to verify an environment setup, the agent calls `check_environment_status` and logs the result directly into your unified, queryable history.

Setup guide

Set up Frontegg MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Frontegg MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

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

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Frontegg tools.",
)
response = await agent.run("List recent Frontegg data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Frontegg. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Frontegg MCP in LlamaIndex

Install `llama-index-tools-mcp` and configure a `BasicMCPClient`. Wrap it in `McpToolSpec` and call the async tool list method to pass it to your FunctionAgent.

Yes. You can configure your agent to periodically run `get_tenant_details` and embed the resulting JSON directly into your vector store for semantic search.

Use the allowed_tools filter when initializing your agent. This prevents the system from accidentally executing destructive actions like `delete_tenant` during routine data syncs.

No, because the agent grounds its answers in real data. By pulling from `list_permissions`, the RAG pipeline relies strictly on the actual API response rather than training weights.

Vinkius operates on a zero-trust architecture. When your application extracts names and emails via `get_user_details`, the connection happens inside an isolated sandbox that drops all state the moment the data transfers. We never store your records.

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