Cheddar MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Cheddar as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 Cheddar. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Cheddar?"
)
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 Cheddar MCP Server
Connect your Cheddar (formerly CheddarGetter) account to any AI agent and take full control of your recurring and usage-based billing through natural conversation. Streamline how you manage subscriptions and tracked items.
LlamaIndex agents combine Cheddar tool responses with indexed documents for comprehensive, grounded answers. Connect 8 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.
What you can do
- Customer Oversight — List and retrieve details for all active and inactive customers natively
- Plan Management — Access and monitor available pricing plans and their configurations flawlessly
- Usage Tracking — Add one-time or quantity-based charges to customer accounts securely
- Invoice Intelligence — List and retrieve details for recent customer invoices and billing history flawlessly
- Transaction Auditing — Access and monitor all billing transactions and payment statuses securely
- Product Analytics — Retrieve core product information, configuration metadata, and active promotions flawlessly
The Cheddar MCP Server exposes 8 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 Cheddar to LlamaIndex via MCP
Follow these steps to integrate the Cheddar 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 8 tools from Cheddar
Why Use LlamaIndex with the Cheddar MCP Server
LlamaIndex provides unique advantages when paired with Cheddar through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Cheddar tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Cheddar tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Cheddar, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Cheddar tools were called, what data was returned, and how it influenced the final answer
Cheddar + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Cheddar MCP Server delivers measurable value.
Hybrid search: combine Cheddar real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Cheddar 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 Cheddar for fresh data
Analytical workflows: chain Cheddar queries with LlamaIndex's data connectors to build multi-source analytical reports
Cheddar MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect Cheddar to LlamaIndex via MCP:
add_cheddar_charge
Add a one-time or quantity-based charge to a customer
get_cheddar_customer_details
Get detailed information for a specific customer
get_cheddar_product_info
Retrieve core product and configuration information
list_cheddar_customers
List all customers for the product
list_cheddar_invoices
List recent customer invoices
list_cheddar_plans
List all available pricing plans
list_cheddar_promotions
List active promotions and coupons
list_cheddar_transactions
List recent billing transactions
Example Prompts for Cheddar in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Cheddar immediately.
"Show me the last 5 invoices in Cheddar."
"Add 10 units of usage for customer 'ACME-123' under charge code 'API_CALLS'."
"List all my available pricing plans in Cheddar."
Troubleshooting Cheddar MCP Server with LlamaIndex
Common issues when connecting Cheddar to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpCheddar + LlamaIndex FAQ
Common questions about integrating Cheddar 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 Cheddar 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 Cheddar to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.