How to Use the Baremetrics MCP in LlamaIndex

Q: How do I load Baremetrics data into a LlamaIndex agent?

You install llama-index-tools-mcp and instantiate the BasicMCPClient. Wrap it in a McpToolSpec to convert tools like getchurnrate into standard LlamaIndex tools for your agent over this MCP link.

Q: Can I index Baremetrics customer lists for semantic search?

Yes, you can run listcustomers and pipe the resulting JSON directly into your LlamaIndex document ingest pipeline. This lets you run vector queries over your customer names and billing details.

Q: Does LlamaIndex support live updates from Baremetrics?

Yes, your agent can call listmetrics dynamically during a query loop. This ensures the answers generated by your RAG pipeline reflect current MRR and churn rates.

Q: What does the connection check tool do?

The getaccountcheck tool verifies that your credentials are valid before you start indexing. It prevents your ingestion pipeline from failing silently due to expired API tokens.

Index live Baremetrics SaaS financial metrics directly into your LlamaIndex vector store using this MCP Server.

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Connect Baremetrics MCP to LlamaIndex

Create your Vinkius account to connect Baremetrics 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 Baremetrics with LlamaIndex

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Index Baremetrics LTV and MRR in LlamaIndex

The `get_ltv` tool pulls customer lifetime value data straight from Baremetrics into your LlamaIndex knowledge base. Your pipeline indexes these raw numbers alongside `get_mrr` outputs to ground your agent's financial answers in real-time data. This MCP Server converts live financial metrics into queryable document nodes. You avoid hallucinations because your RAG application queries actual API outputs instead of relying on static training data.

Query Customer Subscriptions via LlamaIndex RAG

The `list_subscriptions` tool extracts active billing agreements to build a searchable index of your customer base. Your LlamaIndex agent queries this vector store to match user questions with actual subscription records from `get_customer`. You filter these indexes using allowed tool specs to control what data your agent can search. This keeps your financial reports accurate and tightly bound to your actual Baremetrics database.

Build a Searchable Plan Database with LlamaIndex

The `list_plans` tool retrieves your active pricing tiers to feed your semantic search indexes. Your LlamaIndex agent maps these plans to high-level trends retrieved from `list_metrics` to answer complex questions about tier performance. By storing these outputs in your local index, you can run semantic queries on your pricing structures. Vinkius runs the underlying connector in a secure sandbox, keeping your financial metrics isolated.

Setup guide

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

You install `llama-index-tools-mcp` and instantiate the `BasicMCPClient`. Wrap it in a `McpToolSpec` to convert tools like `get_churn_rate` into standard LlamaIndex tools for your agent over this MCP link.

Yes, you can run `list_customers` and pipe the resulting JSON directly into your LlamaIndex document ingest pipeline. This lets you run vector queries over your customer names and billing details.

Yes, your agent can call `list_metrics` dynamically during a query loop. This ensures the answers generated by your RAG pipeline reflect current MRR and churn rates.

The `get_account_check` tool verifies that your credentials are valid before you start indexing. It prevents your ingestion pipeline from failing silently due to expired API tokens.

Your financial data remains strictly within your LlamaIndex application runtime and the secure Vinkius V8 isolate. This MCP setup keeps your customer profiles, plan costs, and recurring revenue metrics safe since no raw records are written to persistent logs.

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