How to Use the Authing MCP in LlamaIndex

Q: How do I configure Authing with LlamaIndex using MCP?

Install llama-index-tools-mcp and instantiate a BasicMCPClient. Wrap that client in McpToolSpec and call await mcptoolspec.totoollistasync(). Pass the resulting list into your FunctionAgent.

Q: Can LlamaIndex filter which Authing tools are exposed?

You control exactly what the agent sees using the allowedtools parameter. If you only want it reading data, just expose the list operations and hide the creation endpoints. This limits the blast radius of any autonomous actions.

Q: Does the Authing LlamaIndex setup support data ingestion via the MCP Server?

Set includeresources=True when configuring your connection. This allows the framework to pull raw configuration files and directory schemas directly into your document store. It treats the API like any other data loader.

Index your identity configurations by connecting Authing to LlamaIndex for semantic search across users and roles.

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Connect Authing MCP to LlamaIndex

Create your Vinkius account to connect Authing 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 Authing with LlamaIndex

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Grounding Queries in Directory Data

Pulling live identity records via the MCP protocol requires the `get_user` and `list_users` tools. LlamaIndex takes those JSON responses and embeds them directly into your vector store. You stop relying on outdated documentation and start querying the actual state of your directory. Ask your system who has access to the billing app. The agent retrieves the current configuration, reads the data, and gives you a factual answer. This eliminates hallucinations because the responses are anchored in real API outputs.

Indexing Authing MCP Server Configurations

The `get_security_settings` tool extracts your current password policies and MFA requirements. You feed this output into a document index alongside your company handbook. Now your internal chat bot can answer compliance questions using the live configuration. Mapping access structures involves running `list_roles` and `list_groups` to build a complete hierarchy. Your RAG application stores these relationships, making it trivial to search for overlapping permissions. You build a searchable knowledge base of your entire security posture.

Auditing via Semantic Search

Security teams need fast answers, which is why you run `get_audit_logs` and index the results. Instead of writing complex SQL queries, you ask plain English questions about recent failed logins. The engine finds the relevant log entries based on semantic similarity. You can also track app usage by combining this with `list_applications`. The system correlates login events with specific registered apps in your index. Finding out which third-party integrations are actively used takes seconds instead of hours.

Setup guide

Set up Authing 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 Authing 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 Authing tools.",
)
response = await agent.run("List recent Authing 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 Authing. 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 Authing MCP in LlamaIndex

Install `llama-index-tools-mcp` and instantiate a `BasicMCPClient`. Wrap that client in `McpToolSpec` and call `await mcp_tool_spec.to_tool_list_async()`. Pass the resulting list into your `FunctionAgent`.

You control exactly what the agent sees using the `allowed_tools` parameter. If you only want it reading data, just expose the list operations and hide the creation endpoints. This limits the blast radius of any autonomous actions.

Set `include_resources=True` when configuring your connection. This allows the framework to pull raw configuration files and directory schemas directly into your document store. It treats the API like any other data loader.

Your index becomes stale until you refresh it. You need to write a script that periodically calls the listing tools and updates the vector store embeddings. Live querying via the agent bypasses the index for real-time checks.

The Vinkius platform handles the API connection through a zero-trust, ephemeral sandbox. When you extract permission resources or security settings, the proxy drops the connection state immediately. Your sensitive identity data only persists inside your own private vector database.

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