How to Use the MENU TIGER MCP in LlamaIndex
Index live MENU TIGER restaurant data directly into LlamaIndex to query menus and order histories with semantic search.
Works with every AI agent you already use
…and any MCP-compatible client
Connect MENU TIGER MCP to LlamaIndex
Create your Vinkius account to connect MENU TIGER 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.
Index MENU TIGER data using LlamaIndex
Stop guessing what is on your digital menu. This MCP Server lets LlamaIndex fetch live menu structures using `get_menu` and index them directly into your vector store. Your agent can then answer complex questions about your offerings without hallucinating prices or ingredients. By indexing the output of `list_items`, you create a queryable knowledge base of your entire culinary catalog. When a customer asks for gluten-free options, the index matches the query against actual data retrieved from `get_item`.
Analyze order trends using LlamaIndex
Use your agent to spot ordering patterns over time. By pulling past transaction records with `list_orders` and indexing the results, your system can identify which dishes sell best during weekend rushes. The agent uses `get_order` to pull specific ticket histories, feeding that structured data directly into your analytical index. This makes it easy to track kitchen performance without exporting CSVs.
Organize restaurant structures with this MCP Server
Keep your restaurant organization clean by indexing your layout. The agent runs `list_categories` and `list_menus` to map out exactly how your dishes are grouped on the physical tables. This mapping helps the LlamaIndex agent route customer inquiries to the correct menu section. It can verify system status using `check_menutiger_status` to ensure the live index matches the active physical menu.
Set up MENU TIGER MCP in LlamaIndex
Prerequisites
- Python 3.10+ installed
-
llama-index-tools-mcppackage - 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 providesBasicMCPClientandMcpToolSpec. - 2
Connect with BasicMCPClient
Point
BasicMCPClientto 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 MENU TIGER MCP tools into nativeFunctionToolobjects that any LlamaIndex agent can use. - 4
Run with any LLM
Create a
FunctionAgentwith the tools and your preferred LLM. SwapOpenAIforAnthropic,Gemini, or any LlamaIndex-supported provider.
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 MENU TIGER tools.",
)
response = await agent.run("List recent MENU TIGER data") Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by MENU TIGER. 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 MENU TIGER MCP in LlamaIndex
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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