How to Use the Domo MCP in LlamaIndex

Index your Domo configuration data into LlamaIndex for searchable, grounded governance insights.

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Connect Domo MCP to LlamaIndex

Create your Vinkius account to connect Domo 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 Domo with LlamaIndex

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Index Domo user state in LlamaIndex

Feed the results of your user queries into a vector store to build a searchable map of your organization. Use `update_user` to keep your index fresh as roles shift. This turns your API data into a knowledge base. You query your own infrastructure as easily as you query documentation.

Map Domo group memberships

Query your group architecture by passing `create_group` metadata into your indexer. You see exactly who belongs where without parsing raw JSON logs. Your RAG application uses these results to ground its reasoning. When you ask about access, it retrieves the actual state of your groups.

Manage Domo accounts as knowledge

Delete stale accounts via `delete_user` and watch your index update. You maintain a clean source of truth that reflects your current environment. Use the tool output to verify access requests. The agent checks the index before deciding if a user needs to be added to a new group.

Setup guide

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

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Common questions about Domo MCP in LlamaIndex

The client executes the tool, and the output is converted into a document for your index. This allows LlamaIndex to reason over your current user list.

Absolutely, once you pass the tool outputs into your vector store. You can then perform semantic searches to find which users belong to specific functional groups.

Your index resides in your chosen vector store. The MCP Server only retrieves user-group associations and identity attributes, ensuring no sensitive content is exposed.

You trigger the update tool, then re-index the output. This ensures your knowledge base stays synchronized with the live state of your environment.

Errors appear in your agent's logs as standard tool output. You should define a fallback to ensure your index doesn't ingest incomplete or failed data.

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