How to Use the Umbrellar MCP in LlamaIndex

Q: How can Umbrellar help LlamaIndex find past claim details?

You use the getclaim tool to fetch a specific record. By indexing this output, your RAG application treats that API response like a document, allowing for highly precise semantic searches later.

Q: Can I use Umbrellar to validate policies within LlamaIndex?

Yes. You run validatepolicy and index the boolean result along with the policy name and ID. This allows your knowledge base to confirm policy existence against a specific order context.

Q: Does Umbrellar support syncing data for LlamaIndex?

The syncproducts tool updates product lists between Shopify and the server. Indexing this result ensures your knowledge base always reflects the most current inventory status.

Index Umbrellar warranty data into a searchable knowledge base for LlamaIndex.

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Connect Umbrellar MCP to LlamaIndex

Create your Vinkius account to connect Umbrellar 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 Umbrellar with LlamaIndex

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Retrieving Policy Details from Historical Data

Use the `get_policy` tool to fetch current policy details, and then index that full JSON response. Later queries can search this indexed data—for instance, asking 'What were the coverage limits for Product X in Q3?' This RAG setup allows your LlamaIndex application to ground answers by searching structured API responses, not relying on general knowledge.

Querying Past Claim Records

Instead of just listing claims with `list_claims`, you index the results. Your application can then answer complex questions like 'Show me all denied claims for Brand Y in 2023.' The output from these tools becomes part of your unified index, making historical service data semantically searchable.

Cross-Referencing Product Statuses

When a user asks about product eligibility, you can run `check_product_eligibility` and immediately pass the result to `get_warranty`. Indexing both results allows LlamaIndex to give a comprehensive answer: 'Yes, it's eligible, and here are the specific plans.' This combination of live API data and document search creates powerful, fact-checked knowledge agents.

Setup guide

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

You use the `get_claim` tool to fetch a specific record. By indexing this output, your RAG application treats that API response like a document, allowing for highly precise semantic searches later.

The server provides structured warranty and service data: product registration records, policy details, list of available warranties, and individual claim statuses.

Yes. You run `validate_policy` and index the boolean result along with the policy name and ID. This allows your knowledge base to confirm policy existence against a specific order context.

The `sync_products` tool updates product lists between Shopify and the server. Indexing this result ensures your knowledge base always reflects the most current inventory status.

It handles structured data points like product IDs, policy names, warranty plan details, and formatted claim records. This is all indexable knowledge for your application.

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