ChartMogul 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 ChartMogul as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
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 ChartMogul. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in ChartMogul?"
)
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 ChartMogul MCP Server
Connect your ChartMogul account to any AI agent and take full control of your subscription analytics through natural conversation. Access real-time SaaS metrics like MRR, ARR, and Churn Rate.
LlamaIndex agents combine ChartMogul tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through 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
- Metrics Oversight — Retrieve all high-level subscription metrics (MRR, ARR, ARPA, ASP) natively
- Growth Intelligence — Access detailed customer count and churn rate data flawlessly
- Customer Deep-Dives — List and retrieve complete profiles for any customer in your database securely
- Data Logistics — List and audit all configured data sources providing information to your account flawlessly
- Revenue Analysis — Track MRR and ARR trends over specific timeframes directly within your workspace
- System Verification — Verify API connectivity and account status using the built-in ping and diagnostic tools
The ChartMogul 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 ChartMogul to LlamaIndex via MCP
Follow these steps to integrate the ChartMogul 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 ChartMogul
Why Use LlamaIndex with the ChartMogul MCP Server
LlamaIndex provides unique advantages when paired with ChartMogul through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine ChartMogul tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain ChartMogul tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query ChartMogul, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what ChartMogul tools were called, what data was returned, and how it influenced the final answer
ChartMogul + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the ChartMogul MCP Server delivers measurable value.
Hybrid search: combine ChartMogul real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query ChartMogul 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 ChartMogul for fresh data
Analytical workflows: chain ChartMogul queries with LlamaIndex's data connectors to build multi-source analytical reports
ChartMogul MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect ChartMogul to LlamaIndex via MCP:
get_arr_metrics
Retrieve Annualized Run Rate metrics
get_customer_count_metrics
Retrieve total customer count metrics over time
get_mogul_customer_details
Get detailed information for a specific customer
get_mrr_metrics
Retrieve Monthly Recurring Revenue metrics
get_subscription_metrics
Retrieve all high-level subscription metrics (MRR, ARR, etc)
list_mogul_customers
List all customers in ChartMogul
list_mogul_data_sources
List all data sources configured in the account
ping_mogul_api
Verify connectivity and authentication with the ChartMogul API
Example Prompts for ChartMogul in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with ChartMogul immediately.
"What is my total MRR for the last 3 months?"
"Show me details for customer UUID 'cust_123456'."
"Get my subscription metrics for 2024-01-01 to 2024-03-31."
Troubleshooting ChartMogul MCP Server with LlamaIndex
Common issues when connecting ChartMogul to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpChartMogul + LlamaIndex FAQ
Common questions about integrating ChartMogul 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 ChartMogul 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 ChartMogul to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.