Gelato MCP Server for LlamaIndex 12 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Gelato as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Gelato. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Gelato?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Gelato MCP Server
Connect your Gelato account to any AI agent to automate your print-on-demand (POD) lifecycle through the Model Context Protocol (MCP). Gelato enables creators and businesses to produce and ship custom products globally without inventory. This MCP server allows you to manage orders, retrieve product catalogs, and track real-time shipping statuses directly through natural conversation.
LlamaIndex agents combine Gelato tool responses with indexed documents for comprehensive, grounded answers. Connect 12 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
Key Features
- Order Management — List all print orders, fetch detailed status metadata, and create new global orders programmatically.
- Product Discovery — Access available product catalogs and retrieve detailed specifications for individual items (product UIDs).
- Fulfillment Tracking — Retrieve tracking numbers and real-time shipment details for every order in your account.
- Pricing & Quoting — Request real-time shipping and production quotes for potential orders across different regions.
- Webhook Visibility — List configured webhooks to ensure your internal systems are receiving real-time production updates.
- Account Oversight — Verify your account metadata and API connectivity to maintain a seamless production workflow.
- Global Fulfillment — Leverage Gelato's massive network of local production partners directly from your chat interface.
The Gelato MCP Server exposes 12 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Gelato to LlamaIndex via MCP
Follow these steps to integrate the Gelato MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 12 tools from Gelato
Why Use LlamaIndex with the Gelato MCP Server
LlamaIndex provides unique advantages when paired with Gelato through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Gelato tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Gelato tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Gelato, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Gelato tools were called, what data was returned, and how it influenced the final answer
Gelato + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Gelato MCP Server delivers measurable value.
Hybrid search: combine Gelato real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Gelato to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Gelato for fresh data
Analytical workflows: chain Gelato queries with LlamaIndex's data connectors to build multi-source analytical reports
Gelato MCP Tools for LlamaIndex (12)
These 12 tools become available when you connect Gelato to LlamaIndex via MCP:
cancel_print_order
Cancel an order
create_print_order
Place new order
get_account_info
Get account identity
get_order_shipments
Track shipments
get_order_status
Get order details
get_product_details
Get product metadata
get_shipping_quote
Request a quote
list_catalog_products
List products in catalog
list_print_orders
List all orders
list_print_webhooks
List webhook configs
list_product_catalogs
List product catalogs
verify_api_connection
Check connection
Example Prompts for Gelato in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Gelato immediately.
"List all my active print orders and their current status."
"Get the shipping details for order 'gelato_12345'."
"List all products in the 'Apparel' catalog (ID: cat_987)."
Troubleshooting Gelato MCP Server with LlamaIndex
Common issues when connecting Gelato to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpGelato + LlamaIndex FAQ
Common questions about integrating Gelato MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Gelato with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Gelato to LlamaIndex
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.