How to Use the Cocktail API MCP in LlamaIndex

Q: Can I index search results from the Cocktail API into LlamaIndex vector stores?

Yes, you can load outputs from searchcocktails directly into your vector index. This lets your RAG setup perform semantic searches on retrieved drink preparations.

Q: How do I filter specific ingredients when querying the Cocktail API in LlamaIndex?

You pass the getcocktailsbyingredients tool to your LlamaIndex FunctionAgent. The agent automatically structures the query based on the user's input before searching the API.

Q: Does the Cocktail API MCP Server work with LlamaIndex asynchronous tool calls?

Yes, you can call getrumcocktails asynchronously using the standard McpToolSpec. This keeps your indexing pipelines fast even when fetching multiple drink categories at once.

Q: How do I limit which drink tools my indexing agent can access?

Use the allowedtools filter when initializing your LlamaIndex MCP tool spec. You can restrict the agent to only call gettequilacocktails while ignoring other spirits.

Index classic cocktail recipes directly into your LlamaIndex vector store for semantic bar assistant queries.

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Connect Cocktail API MCP to LlamaIndex

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Setup Cocktail API with LlamaIndex

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Index gin and vodka recipes into LlamaIndex

The `get_gin_cocktails` tool retrieves a structured list of classic gin recipes directly into your LlamaIndex MCP pipeline. Your system converts these recipes into document nodes, creating a searchable index of herbal drink preparations. By combining this with `get_vodka_cocktails`, your RAG application can answer complex questions about base spirits. You get a queryable database of drink recipes that goes way beyond basic keyword search.

Query ingredient combinations using this MCP Server

The `get_cocktails_by_ingredients` tool resolves matches between your home inventory and professional recipes. Your LlamaIndex agent queries this tool, indexes the results, and performs semantic searches to find the closest drink matches. This MCP Server connection lets your agent combine real-time tool outputs with local text files. It means your RAG pipeline can reference classic ratios from `get_classic_margaritas` to suggest drinks based on what is actually in your cabinet.

Build a searchable classic martini index

The `get_classic_martinis` tool extracts classic martini variations to populate your vector store. LlamaIndex reads these outputs and builds embeddings, letting users query martini styles using natural language. If a user asks for a dry variation, the indexer pulls data from `search_cocktails` to serve the exact recipe. This eliminates hallucinations by grounding your agent's answers in verified cocktail data.

Setup guide

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

Yes, you can load outputs from `search_cocktails` directly into your vector index. This lets your RAG setup perform semantic searches on retrieved drink preparations.

You pass the `get_cocktails_by_ingredients` tool to your LlamaIndex FunctionAgent. The agent automatically structures the query based on the user's input before searching the API.

Yes, you can call `get_rum_cocktails` asynchronously using the standard McpToolSpec. This keeps your indexing pipelines fast even when fetching multiple drink categories at once.

Use the allowed_tools filter when initializing your LlamaIndex MCP tool spec. You can restrict the agent to only call `get_tequila_cocktails` while ignoring other spirits.

Your recipe data and ingredient lists are processed in an ephemeral sandbox. Vinkius never logs the specific cocktail recipes or instructions your LlamaIndex agent retrieves, keeping your custom menus private.

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