How to Use the ChartMogul MCP in LlamaIndex

Index ChartMogul revenue metrics directly into LlamaIndex to build searchable RAG applications over your live SaaS billing data.

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Connect ChartMogul MCP to LlamaIndex

Create your Vinkius account to connect ChartMogul 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 ChartMogul with LlamaIndex

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Indexing SaaS metrics via the MCP Server

Using `get_arr_metrics` and `get_customer_count_metrics` pulls annualized revenue and total user counts straight into your RAG pipeline. LlamaIndex takes these raw JSON responses and embeds them into your vector store. Instead of just answering questions with static documents, your application queries live API data. When a user asks about yearly growth, the system retrieves the exact embedded metrics rather than hallucinating numbers.

Embedding account histories

`get_mogul_customer_details` and `list_mogul_customers` extract individual account histories for semantic search. Your knowledge base updates with real-time subscription statuses and churn indicators. Developers use this to fuse billing data with support tickets or sales transcripts. You query the index to find specific accounts, and the engine correlates their MRR with other embedded business context.

Mapping revenue origins

`list_mogul_data_sources` and `get_subscription_metrics` catalog your billing origins and high-level revenue figures. LlamaIndex maps these structures so your application understands where specific financial data lives. Setting this up requires the specific MCP tools package and a basic client configuration. You pass the tools to your agent with data access enabled via the include resources flag.

Setup guide

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

Pass the server URL to your client and wrap it in the tool spec. Your agent will execute the tools and embed the revenue data for querying.

Yes. You pull SaaS metrics via the tool calls and index them alongside your PDFs or text files. The vector store treats both as searchable context.

No. The agent fetches live data via the MCP Server exactly when the prompt requires it. Your vector store stays grounded in current API responses.

You apply the allowed tools filter when configuring the tool spec. This restricts the agent to specific operations like fetching customer lists or MRR.

This server accesses raw customer lists and subscription metrics. Vinkius executes all requests within a zero-trust architecture, meaning no inbound ports are open and your API keys never leave the encrypted environment.

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