How to Use the Casdoor (IAM) MCP in LlamaIndex

Index identity data into LlamaIndex for searchable Casdoor (IAM) insights.

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Connect Casdoor (IAM) MCP to LlamaIndex

Create your Vinkius account to connect Casdoor (IAM) 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 Casdoor (IAM) with LlamaIndex

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Searchable Casdoor (IAM) User Records

Use `list_users` to pull your identity data and index it directly into your vector store. This lets you query your user base using natural language through LlamaIndex. You no longer need to guess user IDs or organization names. Ask your agent about your user distribution, and it pulls the latest data directly from the source to ground its answers.

Retrieve Casdoor (IAM) Profiles for RAG

Fetch specific user profiles with `get_user` and add them to your LlamaIndex knowledge base. This creates a grounded context for your RAG applications when answering questions about access levels. By injecting this data into your index, you avoid hallucinations. The agent provides answers based on the actual state of your identity provider.

Manage Applications via LlamaIndex

The `list_applications` and `get_application` tools provide the necessary data to map your identity structure inside LlamaIndex. Your agent understands how your applications relate to your users. This setup bridges the gap between your identity configuration and your research documents. It creates a unified index where identity data and project documentation live side-by-side.

Setup guide

Set up Casdoor (IAM) 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 Casdoor (IAM) 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 Casdoor (IAM) tools.",
)
response = await agent.run("List recent Casdoor (IAM) data")

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Common questions about Casdoor (IAM) MCP in LlamaIndex

You run the Casdoor (IAM) tools to extract user lists and organization data. LlamaIndex then parses these outputs into vector embeddings for your search index.

You can. By passing the tools to your FunctionAgent, you allow the agent to query your identity provider in real-time if the answer isn't in your static index.

Yes, it provides the live data interface for your RAG pipeline. You use it to populate the index with current user and organization details.

Your user identity data is indexed locally within your chosen vector store. Vinkius does not store or access the records processed by the server.

It accesses organization identifiers, application configurations, and user profile metadata. These are transmitted only to your local LlamaIndex instance.

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