How to Use the Keycloak MCP in LlamaIndex

Q: Does LlamaIndex support filtering Keycloak tools?

It does. You can use the allowedtools filter to restrict the agent to read-only operations like listrealms for safer RAG workflows.

Index your identity state with LlamaIndex. Turn live Keycloak data into a searchable knowledge base for your AI agents.

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Connect Keycloak MCP to LlamaIndex

Create your Vinkius account to connect Keycloak 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 Keycloak with LlamaIndex

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Searchable identity state

LlamaIndex takes the output of `list_users` or `list_groups` and indexes it for semantic search. You can query your identity configuration using natural language. This turns your static Keycloak data into an active knowledge base. Your agent finds the right user or role by understanding your request rather than matching exact IDs.

Ground your agent in live data

Combine API responses with your internal documentation using `get_realm` and `get_client`. Your agent provides answers grounded in your actual production setup. This eliminates hallucinations regarding your security configuration. When your agent explains a role, it pulls the definition directly from your live server environment.

Build RAG for IAM audits

Use the MCP tool output to populate a vector store. You can query the history of `list_admin_events` to find patterns in your security logs. This helps you build audit-ready applications. Your agent can cross-reference recent changes with your existing security policies, providing a clear report on who changed what and when.

Setup guide

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

You wrap the tools in a tool spec and pass them to your agent. The agent then performs the lookups and indexes the results into your vector store.

It does. You can use the allowed_tools filter to restrict the agent to read-only operations like `list_realms` for safer RAG workflows.

Yes. By indexing your role and group data, your agent can identify misconfigurations or overly permissive access settings across your realms.

The server handles pagination through standard API calls. LlamaIndex then processes these chunks to keep your index manageable and fast.

The server ensures all data is encrypted in transit and only accessed via authorized tools. Your indexed data remains in your private storage, isolated from external access.

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