How to Use the Cheddar MCP in LlamaIndex

Q: Can LlamaIndex agents write data back to Cheddar?

Yes. Your agent can run addcheddarcharge to apply usage fees directly from a chat session. The agent determines the charge amount by reading the user's conversation history and matching it against your pricing rules.

Index your subscription data into LlamaIndex vector stores using the Cheddar MCP Server.

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Connect Cheddar MCP to LlamaIndex

Create your Vinkius account to connect Cheddar 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 Cheddar with LlamaIndex

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Perplexity

Semantic search over Cheddar customer profiles

`get_cheddar_customer_details` extracts customer profiles and subscription histories to feed directly into your LlamaIndex query engine. Your RAG setup parses this live data instead of relying on stale database exports. You can query your customer base using natural language. The agent combines `list_cheddar_customers` with your vector store to find users matching specific usage patterns or subscription tiers.

Context-aware promotion and plan indexing

`list_cheddar_plans` retrieves active pricing configurations and converts them into searchable documents. Your agent uses this index to answer complex user questions about pricing structures and feature availability. Adding `list_cheddar_promotions` to the index lets your agent recommend active discounts during user interactions. The agent checks the vector store first, verifies the promo is active, and outputs the exact coupon code.

Live billing search with LlamaIndex and Cheddar

`list_cheddar_invoices` pulls recent invoice history to ground your RAG agent's answers in actual payment data. This stops your agent from hallucinating billing amounts or payment dates when a user asks about their balance. The agent calls `list_cheddar_transactions` to verify payment statuses before answering customer queries. You get highly accurate, grounded support responses without writing complex SQL join queries.

Setup guide

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

You load the MCP tool spec in your Python code and pass the tools to your function agent. The agent calls `list_cheddar_plans` or customer list tools to retrieve the raw data, which is then indexed into your vector store.

Yes. Your agent can run `add_cheddar_charge` to apply usage fees directly from a chat session. The agent determines the charge amount by reading the user's conversation history and matching it against your pricing rules.

The MCP tool spec fetches live data from the API on demand. This ensures your agent is always looking at the latest information from `get_cheddar_product_info` rather than stale vector embeddings.

Yes, for conversational search. Instead of building complex pipeline infrastructure, your agent queries the API directly using these tools and builds the context on the fly.

Your invoice records and customer plans are protected by Vinkius's zero-trust V8 sandbox. No billing details are saved in the cloud, and all API calls run inside ephemeral containers that destroy themselves after execution.

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