How to Use the Gelato MCP in LlamaIndex

Q: How do I integrate the Gelato MCP with LlamaIndex?

Run pip install llama-index-tools-mcp first. You set up a BasicMCPClient, convert the endpoints using McpToolSpec, and pass the list into your FunctionAgent.

Q: Can LlamaIndex automatically quote Gelato shipping rates?

It can. The agent triggers the getshippingquote tool with the destination and items. It then injects that precise cost into its response instead of hallucinating a number.

Q: How do I filter which Gelato tools my LlamaIndex agent can use?

Use the allowedtools parameter when building your tool list. This restricts the agent to specific read-only actions like checking catalogs, blocking it from accidentally placing orders.

Index print-on-demand catalogs into your LlamaIndex knowledge base with this MCP Server.

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Create your Vinkius account to connect Gelato 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 Gelato with LlamaIndex

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Ingest Print-on-Demand Catalogs

Use the `list_catalog_products` tool to pull raw inventory data straight into your LlamaIndex vector store. Your RAG application converts sizing, material specs, and color options into searchable embeddings. When a user asks about available shirt variants, the agent queries the index instead of guessing. It pulls exact metadata via `get_product_details` to ground the response in live availability.

Semantic Search for Past Orders

The `list_print_orders` tool retrieves historical fulfillment records for your RAG pipeline. Your LlamaIndex setup ingests these JSON responses, making years of purchase history semantically searchable. You can ask your agent to find all delayed shipments from last winter. It cross-references the ingested orders and uses `get_order_shipments` to pull the specific tracking events for that time period.

Query Live Configuration States

Your agent runs the `get_account_info` tool to verify billing and identity settings. LlamaIndex treats this MCP output as immediate context, letting you ask questions about your current account constraints. You can also audit your notification routing by calling `list_print_webhooks`. The agent reads the active configurations and compares them against your internal documentation to spot missing event triggers.

Setup guide

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

Run `pip install llama-index-tools-mcp` first. You set up a `BasicMCPClient`, convert the endpoints using `McpToolSpec`, and pass the list into your `FunctionAgent`.

It can. The agent triggers the `get_shipping_quote` tool with the destination and items. It then injects that precise cost into its response instead of hallucinating a number.

Use the `allowed_tools` parameter when building your tool list. This restricts the agent to specific read-only actions like checking catalogs, blocking it from accidentally placing orders.

The connection itself is stateless. Vinkius provides an ephemeral V8 isolate sandbox for the execution, while LlamaIndex manages the memory and context window locally.

The server requires physical delivery coordinates, recipient names, and payment references to execute fulfillment. Your RAG application processes these details temporarily, and you control whether they get embedded into your vector store.

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