ChartMogul MCP Server for LangChain 8 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect ChartMogul through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"chartmogul": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using ChartMogul, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
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.
LangChain's ecosystem of 500+ components combines seamlessly with ChartMogul through native MCP adapters. Connect 8 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
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 LangChain 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 LangChain via MCP
Follow these steps to integrate the ChartMogul MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 8 tools from ChartMogul via MCP
Why Use LangChain with the ChartMogul MCP Server
LangChain provides unique advantages when paired with ChartMogul through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine ChartMogul MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across ChartMogul queries for multi-turn workflows
ChartMogul + LangChain Use Cases
Practical scenarios where LangChain combined with the ChartMogul MCP Server delivers measurable value.
RAG with live data: combine ChartMogul tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query ChartMogul, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain ChartMogul tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every ChartMogul tool call, measure latency, and optimize your agent's performance
ChartMogul MCP Tools for LangChain (8)
These 8 tools become available when you connect ChartMogul to LangChain 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 LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting ChartMogul to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersChartMogul + LangChain FAQ
Common questions about integrating ChartMogul MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
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 LangChain
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.